GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields
  through Efficient Dense 3D Point Tracking
4D video control is essential in video generation as it enables the use of
sophisticated lens techniques, such as multi-camera shooting and dolly zoom,
which are currently unsupported by existing methods. Training a video Diffusion
Transformer (DiT) directly to control 4D content requires expensive multi-view
videos. Inspired by Monocular Dynamic novel View Synthesis (MDVS) that
optimizes a 4D representation and renders videos according to different 4D
elements, such as camera pose and object motion editing, we bring pseudo 4D
Gaussian fields to video generation. Specifically, we propose a novel framework
that constructs a pseudo 4D Gaussian field with dense 3D point tracking and
renders the Gaussian field for all video frames. Then we finetune a pretrained
DiT to generate videos following the guidance of the rendered video, dubbed as
GS-DiT. To boost the training of the GS-DiT, we also propose an efficient Dense
3D Point Tracking (D3D-PT) method for the pseudo 4D Gaussian field
construction. Our D3D-PT outperforms SpatialTracker, the state-of-the-art
sparse 3D point tracking method, in accuracy and accelerates the inference
speed by two orders of magnitude. During the inference stage, GS-DiT can
generate videos with the same dynamic content while adhering to different
camera parameters, addressing a significant limitation of current video
generation models. GS-DiT demonstrates strong generalization capabilities and
extends the 4D controllability of Gaussian splatting to video generation beyond
just camera poses. It supports advanced cinematic effects through the
manipulation of the Gaussian field and camera intrinsics, making it a powerful
tool for creative video production. Demos are available at
https://wkbian.github.io/Projects/GS-DiT/.
677cbd8ef7134ae168acc1fc
Weikang Bian
claimed_verified
2025-01-07T13:01:59.093Z
65b8b840a81930c593073370
/avatars/31b399d3da33e8c02f9e06a16dd85779.svg
Weikang BIAN
user
wkbian
677cbd8ef7134ae168acc1fd
Zhaoyang Huang
677cbd8ef7134ae168acc1fe
Xiaoyu Shi
677cbd8ef7134ae168acc1ff
Yijin Li
677cbd8ef7134ae168acc200
Fu-Yun Wang
claimed_verified
2025-01-07T08:41:34.222Z
63e9e92f20c109718713f5eb
/avatars/9ff312e854d803e1a2e9e685a21d12f8.svg
Fu-Yun Wang
user
wangfuyun
677cbd8ef7134ae168acc201
Hongsheng Li
GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields
  through Efficient Dense 3D Point Tracking
4D video control is essential in video generation as it enables the use of
sophisticated lens techniques, such as multi-camera shooting and dolly zoom,
which are currently unsupported by existing methods. Training a video Diffusion
Transformer (DiT) directly to control 4D content requires expensive multi-view
videos. Inspired by Monocular Dynamic novel View Synthesis (MDVS) that
optimizes a 4D representation and renders videos according to different 4D
elements, such as camera pose and object motion editing, we bring pseudo 4D
Gaussian fields to video generation. Specifically, we propose a novel framework
that constructs a pseudo 4D Gaussian field with dense 3D point tracking and
renders the Gaussian field for all video frames. Then we finetune a pretrained
DiT to generate videos following the guidance of the rendered video, dubbed as
GS-DiT. To boost the training of the GS-DiT, we also propose an efficient Dense
3D Point Tracking (D3D-PT) method for the pseudo 4D Gaussian field
construction. Our D3D-PT outperforms SpatialTracker, the state-of-the-art
sparse 3D point tracking method, in accuracy and accelerates the inference
speed by two orders of magnitude. During the inference stage, GS-DiT can
generate videos with the same dynamic content while adhering to different
camera parameters, addressing a significant limitation of current video
generation models. GS-DiT demonstrates strong generalization capabilities and
extends the 4D controllability of Gaussian splatting to video generation beyond
just camera poses. It supports advanced cinematic effects through the
manipulation of the Gaussian field and camera intrinsics, making it a powerful
tool for creative video production. Demos are available at
https://wkbian.github.io/Projects/GS-DiT/.
A novel framework combining pseudo 4D Gaussian fields with a video Diffusion Transformer (DiT) allows for more efficient and flexible 4D video generation, supporting advanced cinematic effects and different camera parameters.
Diffusion Transformer (DiT) pseudo 4D Gaussian fields dense 3D point tracking efficient Dense 3D Point Tracking (D3D-PT) Gaussian splatting 4D video control camera poses camera intrinsics video generation cinematic effects dynamic content