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VDT: General-purpose Video Diffusion Transformers via Mask Modeling

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

arXiv:2305.13311v2cs.CV

TL;DR

Video diffusion faces challenges in modeling temporal dependencies and unifying diverse tasks, while prior work has often used U-Net backbones. VDT applies a transformer with spatial-temporal mask modeling across video-generation tasks, and the authors report its potential through quantitative and qualitative evaluation.

  • Problem

    Video diffusion must model temporal dependencies for consistent, high-quality frames and unify tasks including generation, prediction, interpolation, animation, and completion.

    Source: §1 · p. 2

  • Method

    VDT uses transformer blocks with spatial and temporal attention and unified spatial-temporal mask modeling to support diverse video-generation tasks.

    Source: §1 · p. 2

  • Results

    The authors report that VDT captures temporal dependencies, supports diverse video tasks, and shows potential in quantitative and qualitative evaluation.

    Source: §1 · p. 2, §5 · p. 10

  • Takeaways & Limitations

    The authors present VDT as a general-purpose video diffuser whose transformer sequence modeling extends to video prediction through token concatenation.

    Source: §1 · p. 2, §5 · p. 10

    Limited GPU resources prevented pretraining VDT on large-scale image or video datasets, restricting its potential.

    Source: §5 · p. 10

Abstract

from arXiv · show

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

1 INTRODUCTION

Video diffusion faces both a temporal-modeling challenge and the need to unify diverse generation tasks, while prior video diffusion commonly used U-Net backbones. VDT applies a transformer architecture with spatial-temporal mask modeling to address these challenges across tasks.

1 INTRODUCTION

  • Video diffusion must model temporal information for consistent, high-quality frames while unifying unconditional generation, prediction, interpolation, animation, and completion.The introduction identifies these as central challenges for video diffusion.Source: §1 · p. 2
  • Prior video diffusion methods commonly use U-Net backbones, and few studies examine alternative architectures such as transformers.The paper asks whether vision transformers can serve as video diffusion backbones.Source: §1 · p. 2
  • Transformers offer long-range temporal modeling, flexibility across conditioning lengths and modalities, and scalability relevant to video generation.The paper contrasts transformers’ temporal capabilities and scalability with U-Net-based approaches and argues that their tokenization can help unify tasks.Source: §1 · p. 2
  • VDT combines transformer blocks with temporal and spatial attention, a VAE tokenizer, a decoder, and unified spatial-temporal mask modeling for multiple video tasks.The described tasks include unconditional generation, bidirectional forecasting, arbitrary interpolation, and dynamic animation.Source: §1 · p. 2
  • The authors present VDT as a pioneering transformer for diffusion-based video generation and report state-of-the-art performance across tasks, including 3D-object dynamics on physics-QA.They also describe studies of temporal dependencies, conditioning, and efficient training.Source: §1 · pp. 2–3

2 RELATED WORK

Related work spans diffusion models across modalities and video-generation approaches using GANs, quantized latents, or U-Net-based diffusion. The paper positions VDT as a pure-transformer model that extends across video tasks through unified spatial-temporal mask modeling.

2 RELATED WORK

  • Diffusion models have been applied to image, audio, video, and point-cloud generation, while transformer-based diffusion has also been explored for image generation (Peebles & Xie, 2022; Bao et al., 2022).The cited image-generation transformer models achieved results comparable to U-Net-based architectures in that domain.Source: §2 · p. 3
  • Video-generation approaches include GANs, vector-quantized autoencoders followed by transformers, and diffusion methods extending 2D U-Nets with temporal attention.Diffusion-based video prediction has also used specialized modules to generate frames from previously generated frames.Source: §2 · p. 3
  • Prior work has often focused on video generation or prediction separately, whereas VDT uses a pure-transformer architecture and unified spatial-temporal masks across tasks without changing the underlying architecture.The passage describes this broader task coverage as extending to video-generation tasks beyond generation and prediction.Source: §2 · p. 3

3 METHOD

VDT applies a transformer-based diffusion framework to video generation and extends it to conditional and varied generation tasks. Its spatial-temporal mask mechanism unifies different conditioning patterns within the model.

3 METHOD

  • VDT is a unified transformer framework for diffusion-based video generation, with extensions for conditional generation and diverse tasks.The paper adopts a traditional transformer structure and leaves significant architectural modifications for future work.Source: §3 · p. 3, §3.1 · p. 3

3.1 OVERALL FRAMEWORK

  • VDT uses VAE-encoded latent features, spatial and temporal positional embeddings, and transformer blocks with temporal attention, spatial attention, and time-conditioned normalization.The time embedding supplies scale and shift parameters after layer normalization.Source: §3.1 · p. 4

3.2 CONDITIONAL VIDEO GENERATION SCHEME FOR VIDEO PREDICTION

  • For video prediction, VDT explores adaptive layer normalization, cross-attention, and token concatenation; concatenation gives the fastest convergence and superior final outcomes.Concatenation feeds conditional and noisy latent features as tokens, then separates the predicted frames for diffusion.Source: §3.2 · pp. 4–5
  • After training with fixed-length conditional frames, VDT can accept different condition lengths at inference and produce consistent predicted features.The paper reports this transfer without additional training.Source: §3.2 · p. 5

3.3 UNIFIED SPATIAL-TEMPORAL MASK MODELING

  • A spatial-temporal mask unifies conditional inputs by selecting between real-video features and noise, and can be modulated to incorporate different training tasks.The inputs may be pure noise latents or concatenated conditional and noise latents.Source: §3.3 · p. 5
  • The mask framework covers unconditional generation, prediction of past events from final frames, arbitrary-frame interpolation, and image-to-video generation from a chosen temporal position.Image-to-video generation is presented as a specific instance of arbitrary video interpolation.Source: §3.3 · p. 6
  • For spatial-temporal video completion, VDT uses block masks to prevent trivial solutions based on nearby tokens.The authors note that straightforward random spatial-temporal masking may be too simple because surrounding tokens can reveal the missing content.Source: §3.3 · p. 6

4 EXPERIMENT

VDT is evaluated on unconditional generation and video prediction, with analyses of conditioning and training strategies alongside comparisons to prior methods. The reported results span UCF101, Cityscapes, and Physion, with qualitative findings on TaiChi and Sky Time-Lapse.

4 EXPERIMENT

  • The evaluation covers unconditional generation on UCF101, TaiChi, and Sky Time-Lapse, plus prediction on Cityscapes and Physion.FVD is the main comparison metric; generation also reports SSIM and PSNR, while physical prediction reports VQA accuracy.Source: §4.1 · p. 6

4.2 ANALYSIS

  • Token concatenation converges fastest and yields the best sample quality among the three prediction strategies, so the study uses it for video prediction.Sample quality is assessed with FVD and SSIM.Source: §4.2 · p. 8, Table 2 · p. 8
  • Spatial pretraining followed by joint training outperforms direct spatial-temporal joint tuning (431.7 vs. 451.9) with less time (11.2 vs. 14.4).The analysis removes temporal attention and samples one frame per video during spatial-only training.Source: §4.2 · p. 8, Table 3 · p. 8

4.3 COMPARISON TO THE STATE-OF-THE-ARTS

  • VDT achieves coherent, extensive TaiChi motion with intricate detail, while DIGAN and VideoFusion produce mostly static movement and hand distortions.On Sky Time-Lapse, DIGAN and VideoFusion show noise artifacts, whereas VDT has superior color fidelity.Source: §4.3 · pp. 8–9
  • Across tasks, VDT beats GAN-based methods on UCF101, is comparable to MCVD in Cityscapes FVD but superior in SSIM, and exceeds scene-centric methods on Physion VQA.The Cityscapes comparison uses a straightforward token-concatenation strategy.Source: §4.3 · pp. 8–9

5 CONCLUSION

The conclusion presents VDT as a transformer-based video generation model and highlights its straightforward extension to video prediction. The authors note that compute limits prevented large-scale pretraining and identify larger-scale pretraining and text modalities as future directions.

5 CONCLUSION

  • VDT is a video generation model based on a simple transformer architecture.The conclusion describes its sequence modeling capability as enabling extension to video prediction through token concatenation.Source: §5 · p. 10
  • The authors view VDT's quantitative and qualitative evaluation as evidence of its potential to advance video generation and inspire future research.Source: §5 · p. 10
  • GPU-resource limits prevented large-scale image or video pretraining; the authors identify larger-dataset pretraining and adding modalities such as text as future work.The paper also calls for analysis of potential consequences and responsible practices for video generation.Source: §5 · p. 10

A DETAILS OF DOWNSTREAM TASKS

The downstream-task details are organized in a hyperparameter table. The provided passage identifies that table but does not specify individual settings.

A DETAILS OF DOWNSTREAM TASKS

B PHYSICAL VIDEO PREDICTION.

VDT is evaluated on Physion, a demanding benchmark for forecasting physical scenarios, using observed frames to predict future video directly. It shows physical-process understanding and semantic consistency, and outperforms scene-centric methods on the collision VQA test.

B PHYSICAL VIDEO PREDICTION.

  • VDT directly predicts video on Physion, learning physical phenomena from conditional frames rather than extracting objects first.Physion is designed to assess forecasting the temporal evolution of physical scenarios.Source: B PHYSICAL VIDEO PREDICTION · p. 14
  • For physical prediction, VDT uniformly samples 8 observed frames as conditionals and predicts subsequent 8 frames, showing strong understanding of underlying physical processes and maintaining high semantic consistency.Source: B PHYSICAL VIDEO PREDICTION · pp. 14–15
  • VDT outperforms all scene-centric methods on the Physion collision VQA test using observed and predicted frames.A simple MLP determines whether two objects collide.Source: B PHYSICAL VIDEO PREDICTION · p. 15

C ZERO-SHOT ADAPTATION TO LONGER CONDITIONAL FRAMES

VDT trained with 8 conditional frames can be used at inference with different numbers of conditional frames without additional training. Longer conditioning also improves the reported prediction at frame 16.

C ZERO-SHOT ADAPTATION TO LONGER CONDITIONAL FRAMES

  • VDT trained with 8 conditional frames generalizes at inference to condition lengths of 8, 10, 12, and 14 without additional training.The paper describes this as zero-shot transfer to downstream tasks of different lengths.Source: C ZERO-SHOT ADAPTATION TO LONGER… · p. 15
  • The model naturally learns additional information from extended conditional frames.The reported frame-16 prediction is more accurate with 14 conditional frames than with 8.Source: C ZERO-SHOT ADAPTATION TO LONGER… · pp. 15–16
  • In the cited comparison, the prediction from a sample with conditional frame length 14 is more accurate at the 16th frame than the prediction from a sample with conditional frame length 8.Source: C ZERO-SHOT ADAPTATION TO LONGER… · p. 16

D MORE QUALITATIVE RESULTS

The additional qualitative examples span sky time-lapse, TaiChi-HD, Cityscapes, Physion, and UCF101. They illustrate conditional prediction, longer rollouts, varied conditioning lengths, and unconditional generation.

D MORE QUALITATIVE RESULTS

  • The other examples cover Sky Time-Lapse and TaiChi-HD at 16x256x256, and unconditional UCF101 generation at 16x64x64.These are qualitative results.Source: Figure 11 · p. 17, Figure 12 · p. 18, Figure 13 · p. 19
  • On Cityscapes, 2 conditional frames are used to predict 28 frames in one forward pass, with semantic coherence in color and brightness.The results are shown at 16x128x128.Source: Figure 15 · p. 21
  • Physion examples show parabolic ball motion, rolling and collision, and a ball stopping before collision.The examples use 8 conditional frames to predict the next 8.Source: Figure 16 · p. 22
  • A longer Physion rollout predicts 8 frames from 8 conditions, then predicts 8 more from the generated frames.The resulting prediction spans 16 frames.Source: Figure 17 · p. 23
  • Physion inference with condition-frame sizes different from training is reported to generalize without additional training.Training uses 8 condition frames; the caption describes zero-shot transfer to different sizes.Source: Figure 18 · p. 24

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