diff --git a/configure b/configure
index f115b21064..85ce3e67a3 100755
--- a/configure
+++ b/configure
@@ -279,6 +279,7 @@ External library support:
   --enable-libtheora       enable Theora encoding via libtheora [no]
   --enable-libtls          enable LibreSSL (via libtls), needed for https support
                            if openssl, gnutls or mbedtls is not used [no]
+  --enable-libtorch        enable Torch as one DNN backend
   --enable-libtwolame      enable MP2 encoding via libtwolame [no]
   --enable-libuavs3d       enable AVS3 decoding via libuavs3d [no]
   --enable-libv4l2         enable libv4l2/v4l-utils [no]
@@ -1850,6 +1851,7 @@ EXTERNAL_LIBRARY_LIST="
     libopus
     libplacebo
     libpulse
+    libtorch
     librabbitmq
     librav1e
     librist
@@ -2719,7 +2721,7 @@ dct_select="rdft"
 deflate_wrapper_deps="zlib"
 dirac_parse_select="golomb"
 dovi_rpu_select="golomb"
-dnn_suggest="libtensorflow libopenvino"
+dnn_suggest="libtensorflow libopenvino libtorch"
 dnn_deps="avformat swscale"
 error_resilience_select="me_cmp"
 faandct_deps="faan"
@@ -6600,6 +6602,7 @@ enabled libopus           && {
 }
 enabled libplacebo        && require_pkg_config libplacebo "libplacebo >= 4.192.0" libplacebo/vulkan.h pl_vulkan_create
 enabled libpulse          && require_pkg_config libpulse libpulse pulse/pulseaudio.h pa_context_new
+enabled libtorch          && add_cppflags -D_GLIBCXX_USE_CXX11_ABI=0 && check_cxxflags -std=c++14 && require_cpp libtorch torch/torch.h "torch::Tensor" -ltorch -lc10 -ltorch_cpu -lstdc++ -lpthread
 enabled librabbitmq       && require_pkg_config librabbitmq "librabbitmq >= 0.7.1" amqp.h amqp_new_connection
 enabled librav1e          && require_pkg_config librav1e "rav1e >= 0.4.0" rav1e.h rav1e_context_new
 enabled librist           && require_pkg_config librist "librist >= 0.2" librist/librist.h rist_receiver_create
@@ -7025,6 +7028,8 @@ check_disable_warning -Wno-pointer-sign
 check_disable_warning -Wno-unused-const-variable
 check_disable_warning -Wno-bool-operation
 check_disable_warning -Wno-char-subscripts
+#this option is for supress redundant-decls warning in compile libtorch
+check_disable_warning -Wno-redundant-decls
 
 check_disable_warning_headers(){
     warning_flag=-W${1#-Wno-}
diff --git a/libavfilter/dnn/Makefile b/libavfilter/dnn/Makefile
index 4cfbce0efc..d44dcb847e 100644
--- a/libavfilter/dnn/Makefile
+++ b/libavfilter/dnn/Makefile
@@ -16,5 +16,6 @@ OBJS-$(CONFIG_DNN)                           += dnn/dnn_backend_native_layer_mat
 
 DNN-OBJS-$(CONFIG_LIBTENSORFLOW)             += dnn/dnn_backend_tf.o
 DNN-OBJS-$(CONFIG_LIBOPENVINO)               += dnn/dnn_backend_openvino.o
+DNN-OBJS-$(CONFIG_LIBTORCH)                  += dnn/dnn_backend_torch.o
 
 OBJS-$(CONFIG_DNN)                           += $(DNN-OBJS-yes)
diff --git a/libavfilter/dnn/dnn_backend_torch.cpp b/libavfilter/dnn/dnn_backend_torch.cpp
new file mode 100644
index 0000000000..86cc018fbc
--- /dev/null
+++ b/libavfilter/dnn/dnn_backend_torch.cpp
@@ -0,0 +1,567 @@
+/*
+ * Copyright (c) 2022
+ *
+ * This file is part of FFmpeg.
+ *
+ * FFmpeg is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License as published by the Free Software Foundation; either
+ * version 2.1 of the License, or (at your option) any later version.
+ *
+ * FFmpeg is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with FFmpeg; if not, write to the Free Software
+ * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+ */
+
+/**
+ * @file
+ * DNN Torch backend implementation.
+ */
+
+#include <torch/torch.h>
+#include <torch/script.h>
+#include "dnn_backend_torch.h"
+
+extern "C" {
+#include "dnn_io_proc.h"
+#include "../internal.h"
+#include "dnn_backend_common.h"
+#include "libavutil/opt.h"
+#include "queue.h"
+#include "safe_queue.h"
+}
+
+typedef struct THOptions{
+    char *device_name;
+    c10::DeviceType device_type;
+} THOptions;
+
+typedef struct THContext {
+    const AVClass *c_class;
+    THOptions options;
+} THContext;
+
+typedef struct THModel {
+    THContext ctx;
+    DNNModel *model;
+    torch::jit::Module jit_model;
+    SafeQueue *request_queue;
+    Queue *task_queue;
+    Queue *lltask_queue;
+} THModel;
+
+typedef struct THInferRequest {
+    torch::Tensor *output;
+    torch::Tensor *input_tensor;
+} THInferRequest;
+
+typedef struct THRequestItem {
+    THInferRequest *infer_request;
+    LastLevelTaskItem *lltask;
+    DNNAsyncExecModule exec_module;
+} THRequestItem;
+
+
+#define OFFSET(x) offsetof(THContext, x)
+#define FLAGS AV_OPT_FLAG_FILTERING_PARAM
+static const AVOption dnn_th_options[] = {
+    { "device", "device to run model", OFFSET(options.device_name), AV_OPT_TYPE_STRING, { .str = "cpu" }, 0, 0, FLAGS },
+    { NULL }
+};
+
+AVFILTER_DEFINE_CLASS(dnn_th);
+
+static int execute_model_th(THRequestItem *request, Queue *lltask_queue);
+static int th_start_inference(void *args);
+static void infer_completion_callback(void *args);
+
+static int extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
+{
+    THModel *th_model = (THModel *)task->model;
+    THContext *ctx = &th_model->ctx;
+    LastLevelTaskItem *lltask = (LastLevelTaskItem *)av_malloc(sizeof(*lltask));
+    if (!lltask) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for LastLevelTaskItem\n");
+        return AVERROR(ENOMEM);
+    }
+    task->inference_todo = 1;
+    task->inference_done = 0;
+    lltask->task = task;
+    if (ff_queue_push_back(lltask_queue, lltask) < 0) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to push back lltask_queue.\n");
+        av_freep(&lltask);
+        return AVERROR(ENOMEM);
+    }
+    return 0;
+}
+
+static int get_input_th(void *model, DNNData *input, const char *input_name)
+{
+    input->dt = DNN_FLOAT;
+    input->order = DCO_RGB_PLANAR;
+    input->height = -1;
+    input->width = -1;
+    input->channels = 3;
+    return 0;
+}
+
+static int get_output_th(void *model, const char *input_name, int input_width, int input_height,
+                                   const char *output_name, int *output_width, int *output_height)
+{
+    int ret = 0;
+    THModel *th_model = (THModel*) model;
+    THContext *ctx = &th_model->ctx;
+    TaskItem task;
+    THRequestItem *request;
+    DNNExecBaseParams exec_params = {
+        .input_name     = input_name,
+        .output_names   = &output_name,
+        .nb_output      = 1,
+        .in_frame       = NULL,
+        .out_frame      = NULL,
+    };
+    ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, th_model, input_height, input_width, ctx);
+    if ( ret != 0) {
+        goto err;
+    }
+
+    ret = extract_lltask_from_task(&task, th_model->lltask_queue);
+    if ( ret != 0) {
+        av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
+        goto err;
+    }
+
+    request = (THRequestItem*) ff_safe_queue_pop_front(th_model->request_queue);
+    if (!request) {
+        av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
+        ret = AVERROR(EINVAL);
+        goto err;
+    }
+
+    ret = execute_model_th(request, th_model->lltask_queue);
+    *output_width = task.out_frame->width;
+    *output_height = task.out_frame->height;
+
+err:
+    av_frame_free(&task.out_frame);
+    av_frame_free(&task.in_frame);
+    return ret;
+}
+
+static void th_free_request(THInferRequest *request)
+{
+    if (!request)
+        return;
+    if (request->output) {
+        delete(request->output);
+        request->output = NULL;
+    }
+    if (request->input_tensor) {
+        delete(request->input_tensor);
+        request->input_tensor = NULL;
+    }
+    return;
+}
+
+static inline void destroy_request_item(THRequestItem **arg)
+{
+    THRequestItem *item;
+    if (!arg || !*arg) {
+        return;
+    }
+    item = *arg;
+    th_free_request(item->infer_request);
+    av_freep(&item->infer_request);
+    av_freep(&item->lltask);
+    ff_dnn_async_module_cleanup(&item->exec_module);
+    av_freep(arg);
+}
+
+static THInferRequest *th_create_inference_request(void)
+{
+    THInferRequest *request = (THInferRequest *)av_malloc(sizeof(THInferRequest));
+    if (!request) {
+        return NULL;
+    }
+    request->input_tensor = NULL;
+    request->output = NULL;
+    return request;
+}
+
+DNNModel *ff_dnn_load_model_th(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx)
+{
+    DNNModel *model = NULL;
+    THModel *th_model = NULL;
+    THRequestItem *item = NULL;
+    THContext *ctx;
+
+    model = (DNNModel *)av_mallocz(sizeof(DNNModel));
+    if (!model) {
+        return NULL;
+    }
+
+    th_model = (THModel *)av_mallocz(sizeof(THModel));
+    if (!th_model) {
+        av_freep(&model);
+        return NULL;
+    }
+
+    th_model->ctx.c_class = &dnn_th_class;
+    ctx = &th_model->ctx;
+    //parse options
+    av_opt_set_defaults(ctx);
+    if (av_opt_set_from_string(ctx, options, NULL, "=", "&") < 0) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to parse options \"%s\"\n", options);
+        return NULL;
+    }
+
+    c10::Device device = c10::Device(ctx->options.device_name);
+    if (device.is_cpu()) {
+        ctx->options.device_type = torch::kCPU;
+    } else {
+        av_log(ctx, AV_LOG_ERROR, "Not supported device:\"%s\"\n", ctx->options.device_name);
+        goto fail;
+    }
+
+    try {
+        th_model->jit_model = torch::jit::load(model_filename, device);
+    } catch (const c10::Error& e) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to load torch model\n");
+        goto fail;
+    }
+
+    th_model->request_queue = ff_safe_queue_create();
+    if (!th_model->request_queue) {
+        goto fail;
+    }
+
+    item = (THRequestItem *)av_mallocz(sizeof(THRequestItem));
+    if (!item) {
+        goto fail;
+    }
+    item->lltask = NULL;
+    item->infer_request = th_create_inference_request();
+    if (!item->infer_request) {
+        av_log(NULL, AV_LOG_ERROR, "Failed to allocate memory for Torch inference request\n");
+        goto fail;
+    }
+    item->exec_module.start_inference = &th_start_inference;
+    item->exec_module.callback = &infer_completion_callback;
+    item->exec_module.args = item;
+
+    if (ff_safe_queue_push_back(th_model->request_queue, item) < 0) {
+        goto fail;
+    }
+
+    th_model->task_queue = ff_queue_create();
+    if (!th_model->task_queue) {
+        goto fail;
+    }
+
+    th_model->lltask_queue = ff_queue_create();
+    if (!th_model->lltask_queue) {
+        goto fail;
+    }
+
+    th_model->model = model;
+    model->model = th_model;
+    model->get_input = &get_input_th;
+    model->get_output = &get_output_th;
+    model->options = NULL;
+    model->filter_ctx = filter_ctx;
+    model->func_type = func_type;
+    return model;
+
+fail:
+    destroy_request_item(&item);
+    ff_queue_destroy(th_model->task_queue);
+    ff_queue_destroy(th_model->lltask_queue);
+    ff_safe_queue_destroy(th_model->request_queue);
+    av_freep(&th_model);
+    av_freep(&model);
+    av_freep(&item);
+    return NULL;
+}
+
+static int fill_model_input_th(THModel *th_model, THRequestItem *request)
+{
+    LastLevelTaskItem *lltask = NULL;
+    TaskItem *task = NULL;
+    THInferRequest *infer_request = NULL;
+    DNNData input;
+    THContext *ctx = &th_model->ctx;
+    int ret;
+
+    lltask = (LastLevelTaskItem *)ff_queue_pop_front(th_model->lltask_queue);
+    if (!lltask) {
+        ret = AVERROR(EINVAL);
+        goto err;
+    }
+    request->lltask = lltask;
+    task = lltask->task;
+    infer_request = request->infer_request;
+
+    ret = get_input_th(th_model, &input, NULL);
+    if ( ret != 0) {
+        goto err;
+    }
+
+    input.height = task->in_frame->height;
+    input.width = task->in_frame->width;
+    input.data = malloc(input.height * input.width * 3 * sizeof(float));
+    if (!input.data)
+        return AVERROR(ENOMEM);
+    infer_request->input_tensor = new torch::Tensor();
+    infer_request->output = new torch::Tensor();
+
+    switch (th_model->model->func_type) {
+    case DFT_PROCESS_FRAME:
+        if (task->do_ioproc) {
+            if (th_model->model->frame_pre_proc != NULL) {
+                th_model->model->frame_pre_proc(task->in_frame, &input, th_model->model->filter_ctx);
+            } else {
+                ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx);
+            }
+        }
+        break;
+    default:
+        avpriv_report_missing_feature(NULL, "model function type %d", th_model->model->func_type);
+        break;
+    }
+    *infer_request->input_tensor = torch::from_blob(input.data, {1, 1, 3, input.height, input.width},
+                                                    torch::kFloat32);
+    return 0;
+
+err:
+    th_free_request(infer_request);
+    return ret;
+}
+
+static int th_start_inference(void *args)
+{
+    THRequestItem *request = (THRequestItem *)args;
+    THInferRequest *infer_request = NULL;
+    LastLevelTaskItem *lltask = NULL;
+    TaskItem *task = NULL;
+    THModel *th_model = NULL;
+    THContext *ctx = NULL;
+    std::vector<torch::jit::IValue> inputs;
+
+    if (!request) {
+        av_log(NULL, AV_LOG_ERROR, "THRequestItem is NULL\n");
+        return AVERROR(EINVAL);
+    }
+    infer_request = request->infer_request;
+    lltask = request->lltask;
+    task = lltask->task;
+    th_model = (THModel *)task->model;
+    ctx = &th_model->ctx;
+
+    if (!infer_request->input_tensor || !infer_request->output) {
+        av_log(ctx, AV_LOG_ERROR, "input or output tensor is NULL\n");
+        return DNN_GENERIC_ERROR;
+    }
+    inputs.push_back(*infer_request->input_tensor);
+
+    auto parameters = th_model->jit_model.parameters();
+    auto para = *(parameters.begin());
+
+    *infer_request->output = th_model->jit_model.forward(inputs).toTensor();
+
+    return 0;
+}
+
+static void infer_completion_callback(void *args) {
+    THRequestItem *request = (THRequestItem*)args;
+    LastLevelTaskItem *lltask = request->lltask;
+    TaskItem *task = lltask->task;
+    DNNData outputs;
+    THInferRequest *infer_request = request->infer_request;
+    THModel *th_model = (THModel *)task->model;
+    torch::Tensor *output = infer_request->output;
+
+    c10::IntArrayRef sizes = output->sizes();
+    assert(sizes.size == 5);
+    outputs.order = DCO_RGB_PLANAR;
+    outputs.height = sizes.at(3);
+    outputs.width = sizes.at(4);
+    outputs.dt = DNN_FLOAT;
+    outputs.channels = 3;
+
+    switch (th_model->model->func_type) {
+    case DFT_PROCESS_FRAME:
+        if (task->do_ioproc) {
+            outputs.data = output->data_ptr();
+            if (th_model->model->frame_post_proc != NULL) {
+                th_model->model->frame_post_proc(task->out_frame, &outputs, th_model->model->filter_ctx);
+            } else {
+                ff_proc_from_dnn_to_frame(task->out_frame, &outputs, &th_model->ctx);
+            }
+        } else {
+            task->out_frame->width = outputs.width;
+            task->out_frame->height = outputs.height;
+        }
+        break;
+    default:
+        avpriv_report_missing_feature(&th_model->ctx, "model function type %d", th_model->model->func_type);
+        goto err;
+    }
+    task->inference_done++;
+err:
+    th_free_request(infer_request);
+
+    if (ff_safe_queue_push_back(th_model->request_queue, request) < 0) {
+        destroy_request_item(&request);
+        av_log(&th_model->ctx, AV_LOG_ERROR, "Unable to push back request_queue when failed to start inference.\n");
+    }
+}
+
+static int execute_model_th(THRequestItem *request, Queue *lltask_queue)
+{
+    THModel *th_model = NULL;
+    LastLevelTaskItem *lltask;
+    TaskItem *task = NULL;
+    int ret = 0;
+
+    if (ff_queue_size(lltask_queue) == 0) {
+        destroy_request_item(&request);
+        return 0;
+    }
+
+    lltask = (LastLevelTaskItem *)ff_queue_peek_front(lltask_queue);
+    if (lltask == NULL) {
+        av_log(NULL, AV_LOG_ERROR, "Failed to get LastLevelTaskItem\n");
+        ret = AVERROR(EINVAL);
+        goto err;
+    }
+    task = lltask->task;
+    th_model = (THModel *)task->model;
+
+    ret = fill_model_input_th(th_model, request);
+    if ( ret != 0) {
+        goto err;
+    }
+    if (task->async) {
+        avpriv_report_missing_feature(&th_model->ctx, "LibTorch async");
+    } else {
+        ret = th_start_inference((void *)(request));
+        if (ret != 0) {
+            goto err;
+        }
+        infer_completion_callback(request);
+        return (task->inference_done == task->inference_todo) ? 0 : DNN_GENERIC_ERROR;
+    }
+
+err:
+    th_free_request(request->infer_request);
+    if (ff_safe_queue_push_back(th_model->request_queue, request) < 0) {
+        destroy_request_item(&request);
+    }
+    return ret;
+}
+
+int ff_dnn_execute_model_th(const DNNModel *model, DNNExecBaseParams *exec_params)
+{
+    THModel *th_model = (THModel *)model->model;
+    THContext *ctx = &th_model->ctx;
+    TaskItem *task;
+    THRequestItem *request;
+    int ret = 0;
+
+    ret = ff_check_exec_params(ctx, DNN_TH, model->func_type, exec_params);
+    if (ret != 0) {
+        return ret;
+    }
+
+    task = (TaskItem *)av_malloc(sizeof(TaskItem));
+    if (!task) {
+        av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n");
+        return AVERROR(ENOMEM);
+    }
+
+    ret = ff_dnn_fill_task(task, exec_params, th_model, 0, 1);
+    if (ret != 0) {
+        av_freep(&task);
+        av_log(ctx, AV_LOG_ERROR, "unable to fill task.\n");
+        return ret;
+    }
+
+    ret = ff_queue_push_back(th_model->task_queue, task);
+    if (ret < 0) {
+        av_freep(&task);
+        av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\n");
+        return ret;
+    }
+
+    ret = extract_lltask_from_task(task, th_model->lltask_queue);
+    if (ret != 0) {
+        av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
+        return ret;
+    }
+
+    request = (THRequestItem *)ff_safe_queue_pop_front(th_model->request_queue);
+    if (!request) {
+        av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
+        return AVERROR(EINVAL);
+    }
+
+    return execute_model_th(request, th_model->lltask_queue);
+}
+
+
+int ff_dnn_flush_th(const DNNModel *model)
+{
+    THModel *th_model = (THModel *)model->model;
+    THRequestItem *request;
+
+    if (ff_queue_size(th_model->lltask_queue) == 0) {
+        // no pending task need to flush
+        return 0;
+    }
+    request = (THRequestItem *)ff_safe_queue_pop_front(th_model->request_queue);
+    if (!request) {
+        av_log(&th_model->ctx, AV_LOG_ERROR, "unable to get infer request.\n");
+        return AVERROR(EINVAL);
+    }
+
+    return execute_model_th(request, th_model->lltask_queue);
+}
+
+DNNAsyncStatusType ff_dnn_get_result_th(const DNNModel *model, AVFrame **in, AVFrame **out)
+{
+    THModel *th_model = (THModel *)model->model;
+    return ff_dnn_get_result_common(th_model->task_queue, in, out);
+}
+
+void ff_dnn_free_model_th(DNNModel **model)
+{
+    THModel *th_model;
+    if(*model) {
+        th_model = (THModel *) (*model)->model;
+        while (ff_safe_queue_size(th_model->request_queue) != 0) {
+            THRequestItem *item = (THRequestItem *)ff_safe_queue_pop_front(th_model->request_queue);
+            destroy_request_item(&item);
+        }
+        ff_safe_queue_destroy(th_model->request_queue);
+
+        while (ff_queue_size(th_model->lltask_queue) != 0) {
+            LastLevelTaskItem *item = (LastLevelTaskItem *)ff_queue_pop_front(th_model->lltask_queue);
+            av_freep(&item);
+        }
+        ff_queue_destroy(th_model->lltask_queue);
+
+        while (ff_queue_size(th_model->task_queue) != 0) {
+            TaskItem *item = (TaskItem *)ff_queue_pop_front(th_model->task_queue);
+            av_frame_free(&item->in_frame);
+            av_frame_free(&item->out_frame);
+            av_freep(&item);
+        }
+    }
+    av_freep(&th_model);
+    av_freep(model);
+}
diff --git a/libavfilter/dnn/dnn_backend_torch.h b/libavfilter/dnn/dnn_backend_torch.h
new file mode 100644
index 0000000000..5d6a08f85f
--- /dev/null
+++ b/libavfilter/dnn/dnn_backend_torch.h
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2022
+ *
+ * This file is part of FFmpeg.
+ *
+ * FFmpeg is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License as published by the Free Software Foundation; either
+ * version 2.1 of the License, or (at your option) any later version.
+ *
+ * FFmpeg is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with FFmpeg; if not, write to the Free Software
+ * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+ */
+
+/**
+ * @file
+ * DNN inference functions interface for Torch backend.
+ */
+
+#ifndef AVFILTER_DNN_DNN_BACKEND_TORCH_H
+#define AVFILTER_DNN_DNN_BACKEND_TORCH_H
+
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+#include "../dnn_interface.h"
+
+DNNModel *ff_dnn_load_model_th(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx);
+
+int ff_dnn_execute_model_th(const DNNModel *model, DNNExecBaseParams *exec_params);
+DNNAsyncStatusType ff_dnn_get_result_th(const DNNModel *model, AVFrame **in, AVFrame **out);
+int ff_dnn_flush_th(const DNNModel *model);
+
+void ff_dnn_free_model_th(DNNModel **model);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/libavfilter/dnn/dnn_interface.c b/libavfilter/dnn/dnn_interface.c
index 554a36b0dc..6f4e02b481 100644
--- a/libavfilter/dnn/dnn_interface.c
+++ b/libavfilter/dnn/dnn_interface.c
@@ -27,6 +27,7 @@
 #include "dnn_backend_native.h"
 #include "dnn_backend_tf.h"
 #include "dnn_backend_openvino.h"
+#include "dnn_backend_torch.h"
 #include "libavutil/mem.h"
 
 DNNModule *ff_get_dnn_module(DNNBackendType backend_type)
@@ -70,6 +71,17 @@ DNNModule *ff_get_dnn_module(DNNBackendType backend_type)
         return NULL;
     #endif
         break;
+    case DNN_TH:
+    #if (CONFIG_LIBTORCH == 1)
+        dnn_module->load_model = &ff_dnn_load_model_th;
+        dnn_module->execute_model = &ff_dnn_execute_model_th;
+        dnn_module->get_result = &ff_dnn_get_result_th;
+        dnn_module->flush = &ff_dnn_flush_th;
+        dnn_module->free_model = &ff_dnn_free_model_th;
+    #else
+        av_freep(&dnn_module);
+    #endif
+        break;
     default:
         av_log(NULL, AV_LOG_ERROR, "Module backend_type is not native or tensorflow\n");
         av_freep(&dnn_module);
diff --git a/libavfilter/dnn/dnn_io_proc.c b/libavfilter/dnn/dnn_io_proc.c
index 532b089002..cbaa1e601f 100644
--- a/libavfilter/dnn/dnn_io_proc.c
+++ b/libavfilter/dnn/dnn_io_proc.c
@@ -24,10 +24,20 @@
 #include "libavutil/avassert.h"
 #include "libavutil/detection_bbox.h"
 
+static enum AVPixelFormat get_pixel_format(DNNData *data);
+
 int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
 {
     struct SwsContext *sws_ctx;
+    int frame_size = frame->height * frame->width;
+    int linesize[3];
+    void **dst_data, *middle_data;
+    enum AVPixelFormat fmt;
     int bytewidth = av_image_get_linesize(frame->format, frame->width, 0);
+    linesize[0] = frame->linesize[0];
+    dst_data = (void **)frame->data;
+    fmt = get_pixel_format(output);
+
     if (bytewidth < 0) {
         return AVERROR(EINVAL);
     }
@@ -35,6 +45,18 @@ int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
         avpriv_report_missing_feature(log_ctx, "data type rather than DNN_FLOAT");
         return AVERROR(ENOSYS);
     }
+    if (fmt == AV_PIX_FMT_GBRP) {
+        middle_data = malloc(frame_size * 3 * sizeof(uint8_t));
+        if (!middle_data) {
+            av_log(log_ctx, AV_LOG_ERROR, "Failed to malloc memory for middle_data for "
+                    "the conversion fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
+                    av_get_pix_fmt_name(AV_PIX_FMT_GRAYF32),  frame->width, frame->height,
+                    av_get_pix_fmt_name(AV_PIX_FMT_GRAY8),frame->width, frame->height);
+            return AVERROR(EINVAL);
+        }
+        dst_data = &middle_data;
+        linesize[0] = frame->width * 3;
+    }
 
     switch (frame->format) {
     case AV_PIX_FMT_RGB24:
@@ -51,12 +73,43 @@ int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
                 "fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
                 av_get_pix_fmt_name(AV_PIX_FMT_GRAYF32), frame->width * 3, frame->height,
                 av_get_pix_fmt_name(AV_PIX_FMT_GRAY8),   frame->width * 3, frame->height);
+            av_freep(&middle_data);
             return AVERROR(EINVAL);
         }
         sws_scale(sws_ctx, (const uint8_t *[4]){(const uint8_t *)output->data, 0, 0, 0},
                            (const int[4]){frame->width * 3 * sizeof(float), 0, 0, 0}, 0, frame->height,
-                           (uint8_t * const*)frame->data, frame->linesize);
+                           (uint8_t * const*)dst_data, linesize);
         sws_freeContext(sws_ctx);
+        switch (fmt) {
+        case AV_PIX_FMT_GBRP:
+            sws_ctx = sws_getContext(frame->width,
+                                     frame->height,
+                                     AV_PIX_FMT_GBRP,
+                                     frame->width,
+                                     frame->height,
+                                     frame->format,
+                                     0, NULL, NULL, NULL);
+            if (!sws_ctx) {
+                av_log(log_ctx, AV_LOG_ERROR, "Impossible to create scale context for the conversion "
+                       "fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
+                       av_get_pix_fmt_name(AV_PIX_FMT_GBRP),  frame->width, frame->height,
+                       av_get_pix_fmt_name(frame->format),frame->width, frame->height);
+                av_freep(&middle_data);
+                return AVERROR(EINVAL);
+            }
+            sws_scale(sws_ctx, (const uint8_t * const[4]){(uint8_t *)dst_data[0] + frame_size * sizeof(uint8_t),
+                                                          (uint8_t *)dst_data[0] + frame_size * sizeof(uint8_t) * 2,
+                                                          (uint8_t *)dst_data[0], 0},
+                      (const int [4]){frame->width * sizeof(uint8_t),
+                                      frame->width * sizeof(uint8_t),
+                                      frame->width * sizeof(uint8_t), 0}
+                      , 0, frame->height,
+                      (uint8_t * const*)frame->data, frame->linesize);
+            break;
+        default:
+            break;
+        }
+        av_freep(&middle_data);
         return 0;
     case AV_PIX_FMT_GRAYF32:
         av_image_copy_plane(frame->data[0], frame->linesize[0],
@@ -101,6 +154,14 @@ int ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx)
 {
     struct SwsContext *sws_ctx;
     int bytewidth = av_image_get_linesize(frame->format, frame->width, 0);
+    int frame_size = frame->height * frame->width;
+    int linesize[3];
+    void **src_data, *middle_data = NULL;
+    enum AVPixelFormat fmt;
+    linesize[0] = frame->linesize[0];
+    src_data = (void **)frame->data;
+    fmt = get_pixel_format(input);
+
     if (bytewidth < 0) {
         return AVERROR(EINVAL);
     }
@@ -112,6 +173,46 @@ int ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx)
     switch (frame->format) {
     case AV_PIX_FMT_RGB24:
     case AV_PIX_FMT_BGR24:
+        switch (fmt) {
+        case AV_PIX_FMT_GBRP:
+            middle_data = av_malloc(frame_size * 3 * sizeof(uint8_t));
+            if (!middle_data) {
+                av_log(log_ctx, AV_LOG_ERROR, "Failed to malloc memory for middle_data for "
+                       "the conversion fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
+                       av_get_pix_fmt_name(frame->format),  frame->width, frame->height,
+                       av_get_pix_fmt_name(AV_PIX_FMT_GBRP),frame->width, frame->height);
+                return AVERROR(EINVAL);
+            }
+            sws_ctx = sws_getContext(frame->width,
+                                     frame->height,
+                                     frame->format,
+                                     frame->width,
+                                     frame->height,
+                                     AV_PIX_FMT_GBRP,
+                                     0, NULL, NULL, NULL);
+            if (!sws_ctx) {
+                av_log(log_ctx, AV_LOG_ERROR, "Impossible to create scale context for the conversion "
+                       "fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
+                       av_get_pix_fmt_name(frame->format),  frame->width, frame->height,
+                       av_get_pix_fmt_name(AV_PIX_FMT_GBRP),frame->width, frame->height);
+                av_freep(&middle_data);
+                return AVERROR(EINVAL);
+            }
+            sws_scale(sws_ctx, (const uint8_t **)frame->data,
+                      frame->linesize, 0, frame->height,
+                      (uint8_t * const [4]){(uint8_t *)middle_data + frame_size * sizeof(uint8_t),
+                                            (uint8_t *)middle_data + frame_size * sizeof(uint8_t) * 2,
+                                            (uint8_t *)middle_data, 0},
+                      (const int [4]){frame->width * sizeof(uint8_t),
+                                      frame->width * sizeof(uint8_t),
+                                      frame->width * sizeof(uint8_t), 0});
+            sws_freeContext(sws_ctx);
+            src_data = &middle_data;
+            linesize[0] = frame->width * 3;
+            break;
+        default:
+            break;
+        }
         sws_ctx = sws_getContext(frame->width * 3,
                                  frame->height,
                                  AV_PIX_FMT_GRAY8,
@@ -124,13 +225,15 @@ int ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx)
                 "fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
                 av_get_pix_fmt_name(AV_PIX_FMT_GRAY8),  frame->width * 3, frame->height,
                 av_get_pix_fmt_name(AV_PIX_FMT_GRAYF32),frame->width * 3, frame->height);
+            av_freep(&middle_data);
             return AVERROR(EINVAL);
         }
-        sws_scale(sws_ctx, (const uint8_t **)frame->data,
-                           frame->linesize, 0, frame->height,
+        sws_scale(sws_ctx, (const uint8_t **)src_data,
+                           linesize, 0, frame->height,
                            (uint8_t * const [4]){input->data, 0, 0, 0},
                            (const int [4]){frame->width * 3 * sizeof(float), 0, 0, 0});
         sws_freeContext(sws_ctx);
+        av_freep(&middle_data);
         break;
     case AV_PIX_FMT_GRAYF32:
         av_image_copy_plane(input->data, bytewidth,
@@ -184,6 +287,14 @@ static enum AVPixelFormat get_pixel_format(DNNData *data)
             av_assert0(!"unsupported data pixel format.\n");
             return AV_PIX_FMT_BGR24;
         }
+    } else if (data->dt == DNN_FLOAT) {
+        switch (data->order) {
+        case DCO_RGB_PLANAR:
+            return AV_PIX_FMT_GBRP;
+        default:
+            av_assert0(!"unsupported data pixel format.\n");
+            return AV_PIX_FMT_GBRP;
+        }
     }
 
     av_assert0(!"unsupported data type.\n");
diff --git a/libavfilter/dnn_filter_common.c b/libavfilter/dnn_filter_common.c
index 5083e3de19..a4e1147fb9 100644
--- a/libavfilter/dnn_filter_common.c
+++ b/libavfilter/dnn_filter_common.c
@@ -53,19 +53,31 @@ static char **separate_output_names(const char *expr, const char *val_sep, int *
 
 int ff_dnn_init(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *filter_ctx)
 {
+    DNNBackendType backend = ctx->backend_type;
+
     if (!ctx->model_filename) {
         av_log(filter_ctx, AV_LOG_ERROR, "model file for network is not specified\n");
         return AVERROR(EINVAL);
     }
-    if (!ctx->model_inputname) {
-        av_log(filter_ctx, AV_LOG_ERROR, "input name of the model network is not specified\n");
-        return AVERROR(EINVAL);
-    }
 
-    ctx->model_outputnames = separate_output_names(ctx->model_outputnames_string, "&", &ctx->nb_outputs);
-    if (!ctx->model_outputnames) {
-        av_log(filter_ctx, AV_LOG_ERROR, "could not parse model output names\n");
-        return AVERROR(EINVAL);
+    if (backend == DNN_TH) {
+        if (ctx->model_inputname)
+            av_log(filter_ctx, AV_LOG_WARNING, "LibTorch backend do not require inputname, "\
+                                               "inputname will be ignored.\n");
+        if (ctx->model_outputnames)
+            av_log(filter_ctx, AV_LOG_WARNING, "LibTorch backend do not require outputname(s), "\
+                                               "all outputname(s) will be ignored.\n");
+        ctx->nb_outputs = 1;
+    } else {
+        if (!ctx->model_inputname) {
+            av_log(filter_ctx, AV_LOG_ERROR, "input name of the model network is not specified\n");
+            return AVERROR(EINVAL);
+        }
+        ctx->model_outputnames = separate_output_names(ctx->model_outputnames_string, "&", &ctx->nb_outputs);
+        if (!ctx->model_outputnames) {
+            av_log(filter_ctx, AV_LOG_ERROR, "could not parse model output names\n");
+            return AVERROR(EINVAL);
+        }
     }
 
     ctx->dnn_module = ff_get_dnn_module(ctx->backend_type);
@@ -113,8 +125,9 @@ int ff_dnn_get_input(DnnContext *ctx, DNNData *input)
 
 int ff_dnn_get_output(DnnContext *ctx, int input_width, int input_height, int *output_width, int *output_height)
 {
+    const char *model_outputnames = ctx->backend_type == DNN_TH ? NULL : ctx->model_outputnames[0];
     return ctx->model->get_output(ctx->model->model, ctx->model_inputname, input_width, input_height,
-                                    (const char *)ctx->model_outputnames[0], output_width, output_height);
+                                  model_outputnames, output_width, output_height);
 }
 
 int ff_dnn_execute_model(DnnContext *ctx, AVFrame *in_frame, AVFrame *out_frame)
diff --git a/libavfilter/dnn_interface.h b/libavfilter/dnn_interface.h
index d94baa90c4..32698f788b 100644
--- a/libavfilter/dnn_interface.h
+++ b/libavfilter/dnn_interface.h
@@ -32,7 +32,7 @@
 
 #define DNN_GENERIC_ERROR FFERRTAG('D','N','N','!')
 
-typedef enum {DNN_NATIVE, DNN_TF, DNN_OV} DNNBackendType;
+typedef enum {DNN_NATIVE, DNN_TF, DNN_OV, DNN_TH} DNNBackendType;
 
 typedef enum {DNN_FLOAT = 1, DNN_UINT8 = 4} DNNDataType;
 
@@ -40,6 +40,7 @@ typedef enum {
     DCO_NONE,
     DCO_BGR_PACKED,
     DCO_RGB_PACKED,
+    DCO_RGB_PLANAR,
 } DNNColorOrder;
 
 typedef enum {
diff --git a/libavfilter/vf_dnn_processing.c b/libavfilter/vf_dnn_processing.c
index cac096a19f..ac1dc6e1d9 100644
--- a/libavfilter/vf_dnn_processing.c
+++ b/libavfilter/vf_dnn_processing.c
@@ -52,6 +52,9 @@ static const AVOption dnn_processing_options[] = {
 #endif
 #if (CONFIG_LIBOPENVINO == 1)
     { "openvino",    "openvino backend flag",      0,                        AV_OPT_TYPE_CONST,     { .i64 = 2 },    0, 0, FLAGS, "backend" },
+#endif
+#if (CONFIG_LIBTORCH == 1)
+    { "torch",       "torch backend flag",         0,                        AV_OPT_TYPE_CONST,     { .i64 = 3 },    0, 0, FLAGS, "backend" },
 #endif
     DNN_COMMON_OPTIONS
     { NULL }
