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Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach
Yaofang Liu, Yumeng Ren, Xiaodong Cun, Aitor Artola, Yang Liu, Tieyong Zeng, Raymond H. Chan, Jean-michel Morel
TL;DR
Current video diffusion models use a shared scalar timestep, limiting their ability to represent complex frame-level temporal dependencies. FVDM replaces it with vectorized per-frame timesteps, enabling independent noise schedules across frames. The model reports superior video quality and broad performance across standard and extended video-generation tasks.
Problem
Conventional video diffusion models apply a scalar timestep uniformly across frames, limiting modeling of complex temporal dependencies and flexibility for extended tasks.
Source: §1 · p. 2, §2.2.1 · p. 3Method
FVDM introduces a vectorized timestep variable that enables independent frame evolution and noise perturbation within a video diffusion process.
Source: §1 · p. 2, §2.2.1 · p. 3Results
FVDM exceeds current state-of-the-art methods in standard video quality and excels across image-to-video, interpolation, and long-video generation tasks.
Source: §1 · p. 2, §5 · pp. 10–11Takeaways & Limitations
Flexible VTV configurations provide a robust framework for temporally detailed video synthesis across multiple generation tasks without retraining.
Source: §1 · p. 2
Abstract
from arXiv · showhide
Diffusion models have revolutionized image generation, and their extension to video generation has shown promise. However, current video diffusion models~(VDMs) rely on a scalar timestep variable applied at the clip level, which limits their ability to model complex temporal dependencies needed for various tasks like image-to-video generation. To address this limitation, we propose a frame-aware video diffusion model~(FVDM), which introduces a novel vectorized timestep variable~(VTV). Unlike conventional VDMs, our approach allows each frame to follow an independent noise schedule, enhancing the model's capacity to capture fine-grained temporal dependencies. FVDM's flexibility is demonstrated across multiple tasks, including standard video generation, image-to-video generation, video interpolation, and long video synthesis. Through a diverse set of VTV configurations, we achieve superior quality in generated videos, overcoming challenges such as catastrophic forgetting during fine-tuning and limited generalizability in zero-shot methods.Our empirical evaluations show that FVDM outperforms state-of-the-art methods in video generation quality, while also excelling in extended tasks. By addressing fundamental shortcomings in existing VDMs, FVDM sets a new paradigm in video synthesis, offering a robust framework with significant implications for generative modeling and multimedia applications.
1 INTRODUCTION
Existing video diffusion models apply one scalar timestep uniformly across frames, limiting temporal modeling and complicating specialized video tasks. FVDM introduces independent frame evolution through vectorized timesteps, supporting broader applications and improved reported video quality.
Problem
- Conventional VDMs treat a video as a monolithic entity with one scalar timestep, which limits flexibility for nuanced temporal dynamics.This approach works for short clips but fails to capture more sophisticated temporal structures.Source: §1 · p. 2
Related work
- Existing task-specific adaptations use fine-tuning or zero-shot techniques but face catastrophic forgetting or limited generalizability.These limitations can prevent effective use of pretrained VDM latent capabilities across image-to-video, interpolation, and long-video tasks.Source: §1 · p. 2
Contribution
- FVDM uses a vectorized timestep variable to let frames follow independent temporal trajectories, improving modeling of intricate temporal dependencies.Unlike scalar-timestep VDMs, each frame evolves independently during the forward process while the reverse process recovers the complete video sequence.Source: §1 · p. 2
Applications
- FVDM supports standard video synthesis, image-to-video transitions, video interpolation, and long video generation through flexible VTV configurations without retraining.The same framework is presented as applicable across multiple extended video-generation tasks.Source: §1 · p. 2
Results
- Empirical evaluations show FVDM exceeds current state-of-the-art methods in standard video quality and excels across extended applications.The reported results emphasize the framework’s robustness and versatility across video-generation settings.Source: §1 · p. 2
2 METHODS
FVDM replaces the scalar timestep used uniformly across video frames with vectorized, frame-specific timesteps, enabling independent noise evolution and broader zero-shot video applications. Its implementation combines vectorized timestep conditioning with probabilistic sampling while retaining standard diffusion inference schedules.
2.1 PRELIMINARIES: DIFFUSION MODELS
- Diffusion models generate data by adding noise through an SDE and reversing that process with a score function for sampling.The reverse process trains a score model and integrates it into the empirical probability-flow ODE.Source: §2.1 · p. 3
2.2 FRAME-AWARE VIDEO DIFFUSION MODEL
- FVDM introduces a vectorized timestep variable that lets each video frame evolve independently, addressing the limited temporal modeling of scalar-timestep VDMs.Each frame follows its own temporal trajectory during the forward process while the reverse process reconstructs the complete video sequence.Source: §2.2.1 · p. 3
2.2.1 VECTORIZED TIMESTEP VARIABLE
- The vectorized timestep τ(t) assigns one time variable to each of N frames, enabling independent noise perturbations within a unified video representation.The video is represented as X ∈ R^N×d, with frame-specific timestep values collected into a vector.Source: §2.2.1 · p. 3, §2.2.2 · p. 4
2.2.2 FORWARD SDE WITH INDEPENDENT NOISE SCALES
- The forward video SDE uses frame-specific noise scales, represented jointly with a diagonal diffusion matrix so each frame receives independent Gaussian noise.The noise schedule β is non-negative and non-decreasing, with β(0)=0.1 and β(T)=20.Source: §2.2.2 · p. 4
2.2.3 REVERSE SDE AND SCORE FUNCTION
- The reverse process models dependencies across joint frames with a video-level score function and optimizes parameters to approximate the corresponding score.The integrated reverse SDE uses an N-dimensional Brownian motion.Source: §2.2.3 · p. 4
2.3 IMPLEMENTATION
- FVDM adapts existing video diffusion backbones by replacing scalar timestep inputs with frame-wise embeddings that condition transformer attention and MLP layers.Probabilistic timestep sampling mixes distinct per-frame timesteps with shared timesteps, avoiding the 1000^N training combinations of naive sampling; standard DDPM and DDIM schedules remain usable at inference.Source: §2.3 · p. 5
2.4 APPLICATIONS
- VTV configurations support standard synthesis, image-to-video generation, video interpolation, long-video continuation, and other zero-shot applications without retraining.Examples include keeping selected frames noise-free while applying regular noise to remaining frames, enabling conditioning on images, endpoints, or previously generated frames.Source: §2.4 · p. 6
3 EXPERIMENTS
Experiments evaluate FVDM across four datasets, ablations, standard video generation, and zero-shot downstream tasks. FVDM achieves strong benchmark performance and supports coherent image-to-video, interpolation, and long-video synthesis without task-specific retraining.
3.3 STANDARD VIDEO GENERATION
- FVDM produces more coherent and realistic video sequences than Latte across FaceForensics, SkyTimelapse, UCF101, and Taichi-HD qualitative comparisons.The samples are matched by class for UCF101 or similar content for the other datasets.Source: Figure 4 · p. 8
3.2 ABLATION STUDY
- At 200k training steps, sampling probability p = 0.2 achieves FVD 74.31, outperforming Latte’s 82.28 and other tested probabilities.The same probability consistently yields the best results across training iterations.Source: §3.2 · p. 8
- DDPM-250 and DDIM-100 or DDIM-50 perform comparably, whereas DDIM-10 degrades quality; DDIM-50 is selected for efficiency-performance balance.DDIM-100 is slightly better at 200k steps, while DDIM-10 shows significant degradation.Source: §3.2 · p. 8
- Increasing model scale improves performance, with the XL model achieving the best FVD of 57.25 among S, B, L, and XL variants.The evaluated sizes contain 32.59M, 129.76M, 457.09M, and 674.00M parameters, respectively.Source: §3.2 · p. 8
3.3 STANDARD VIDEO GENERATION
- FVDM achieves the lowest FVD on FaceForensics and UCF101 and the second-lowest scores on the other evaluated datasets.The comparison uses four datasets and reports lower FVD as better.Source: Table 1 · p. 7, §3.3 · p. 8
3.4 ZERO-SHOT APPLICATIONS OF FVDM
- Without task-specific training, FVDM generates coherent image-to-video sequences, smooth interpolations, and 128-frame videos with consistent motion and expression.These zero-shot demonstrations cover animation from one image, interpolation between endpoints, and long-video generation.Source: §3.4 · pp. 9–10
4 RELATED WORK
Related work addresses video temporal-modeling limitations through task-specific fine-tuning and zero-shot adaptations. These approaches target image-to-video generation, interpolation, and long-video synthesis using diverse conditioning, diffusion, attention, and rescheduling strategies.
Overview
- Across these task-specific directions, prior approaches predominantly rely on fine-tuning or zero-shot techniques to address conventional VDM temporal-modeling limitations.The related-work survey frames these as the two primary adaptation strategies.Source: §4 · p. 10
Image-to-Video Generation
- Image-to-video methods use fine-tuning, adapters, cascaded diffusion, latent-noise rectification, or zero-shot image conditioning to animate images.Examples include DynamiCrafter, I2V-Adapter, I2VGen-XL, Li et al., and TI2V-Zero.Source: §4 · p. 10
Video Interpolation
- Video interpolation methods formulate the task through conditional diffusion, latent diffusion, generative inbetweening, or ControlNet transfer for controllable generation.MCVD, LDMVFI, generative inbetweening, and EasyControl represent these directions.Source: §4 · p. 10
Long Video Generation
- Long-video methods extend duration through post-tuning, autoregressive conditioning, random masking, noise rescheduling, iterative denoising, or temporal-attention blending.The surveyed methods include both additional-training and no-additional-training approaches.Source: §4 · p. 10
5 CONCLUSIONS
The conclusion presents FVDM as a frame-aware video diffusion model using vectorized timesteps for independent frame evolution. It reports improved quality and flexibility across video tasks, while identifying future extensions in training schemes and VTV configurations.
5 CONCLUSIONS
- FVDM uses a vectorized timestep variable to enable independent frame evolution and address limitations in existing video diffusion models.The conclusion identifies finer temporal modeling as the central design contribution.Source: §5 · p. 10
- Experiments report improved video quality and flexibility across image-to-video generation, video interpolation, and long video synthesis.The conclusion also characterizes FVDM as adaptable and robust relative to state-of-the-art models.Source: §5 · p. 10
- Future extensions include improved training schemes and alternative VTV configurations for tasks such as video infilling.The conclusion presents these as potential directions rather than established capabilities.Source: §5 · p. 11