Papers › VDT: General-purpose Video Diffusion Transformers via Mask Modeling

VDT: General-purpose Video Diffusion Transformers via Mask Modeling

22 May 2023arXiv:2305.13311archive 2025-07-28

Haoyu Lu, Guoxing Yang, Nanyi Fei, Yuqi Huo, Zhiwu Lu, Ping Luo, Mingyu Ding

This work introduces Video Diffusion Transformer (VDT), which pioneers the use of transformers in diffusion-based video generation. It features transformer blocks with modularized temporal and spatial attention modules to leverage the rich spatial-temporal representation inherited in transformers. We also propose a unified spatial-temporal mask modeling mechanism, seamlessly integrated with the model, to cater to diverse video generation scenarios. VDT offers several appealing benefits. 1) It excels at capturing temporal dependencies to produce temporally consistent video frames and even simulate the physics and dynamics of 3D objects over time. 2) It facilitates flexible conditioning information, \eg, simple concatenation in the token space, effectively unifying different token lengths and modalities. 3) Pairing with our proposed spatial-temporal mask modeling mechanism, it becomes a general-purpose video diffuser for harnessing a range of tasks, including unconditional generation, video prediction, interpolation, animation, and completion, etc. Extensive experiments on these tasks spanning various scenarios, including autonomous driving, natural weather, human action, and physics-based simulation, demonstrate the effectiveness of VDT. Additionally, we present comprehensive studies on how \model handles conditioning information with the mask modeling mechanism, which we believe will benefit future research and advance the field. Project page: https:VDT-2023.github.io

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2ran · honoured contract
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FinalLayer rerv/vdt/models.py official repository ran · metamorphic tier: invariant licence not identified · pointer only · 8e42700c31523a9c · report
VDTBlock rerv/vdt/models.py official repository ran licence not identified · pointer only · 0bfa350bbc9cf0ac · report
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normal_kl rerv/vdt/diffusion/diffusion_utils.py official repository ran · honoured contract fingerprinted licence not identified · pointer only · 8afbfc42c6ea0448 · report
VDT rerv/vdt/models.py official repository unverified licence not identified · pointer only · cd8427c86e5448ba · report
approx_standard_normal_cdf identical code first harvested elsewhere ran · our draft was wrong fingerprinted licence of this copy not recorded · d6a68e210556f857 · report
drop_path identical code first harvested elsewhere ran · fixture could not drive it fingerprinted licence of this copy not recorded · 3ac6b7d76e8e3584 · report
get_2d_sincos_pos_embed identical code first harvested elsewhere ran · honoured contract licence of this copy not recorded · c92c27c924b517e8 · report
modulate identical code first harvested elsewhere ran · fixture could not drive it licence of this copy not recorded · c081e2d896c9bd7b · report

Tasks

Autonomous DrivingVideo GenerationVideo Prediction

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDiffusionDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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