Adaptive Begin-of-Video Tokens for Autoregressive Video Diffusion Models

• URL: http://arxiv.org/abs/2511.12099v1
• Date: Sat, 15 Nov 2025 08:29:14 GMT
• Title: Adaptive Begin-of-Video Tokens for Autoregressive Video Diffusion Models
• Authors: Tianle Cheng, Zeyan Zhang, Kaifeng Gao, Jun Xiao,
• Abstract summary: Most video diffusion models (VDMs) generate videos in an autoregressive manner, generating subsequent iteration frames conditioned on previous ones.<n>We propose Adaptive Begin-of-Video Tokens (ada-BOV) for autoregressive VDMs.
• Score: 11.913945404405865
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advancements in diffusion-based video generation have produced impressive and high-fidelity short videos. To extend these successes to generate coherent long videos, most video diffusion models (VDMs) generate videos in an autoregressive manner, i.e., generating subsequent frames conditioned on previous ones. There are generally two primary paradigms: chunk-based extension and stream denoising. The former directly concatenates previous clean frames as conditioning, suffering from denoising latency and error accumulation. The latter maintains the denoising sequence with monotonically increasing noise levels. In each denoising iteration, one clean frame is produced while a new pure noise is simultaneously appended, enabling live-stream sampling. However, it struggles with fragile consistency and poor motion dynamics. In this paper, we propose Adaptive Begin-of-Video Tokens (ada-BOV) for autoregressive VDMs. The BOV tokens are special learnable embeddings on VDMs. They adaptively absorb denoised preceding frames via an adaptive-layer-norm-like modulation. This design preserves the global consistency while allowing for flexible conditioning in dynamic scenarios. To ensure the quality of local dynamics essential in modulating BOV tokens, we further propose a refinement strategy for stream denoising. It decouples the sampling trajectory length from the attention window size constraint, leading to improved local guidance and overall imaging quality. We also propose a disturbance-augmented training noise schedule, which balances the convergence speed with model robustness for the stream denoising. Extensive experiments demonstrate that our method achieves compelling qualitative and quantitative results across multiple metrics.

Related papers

• Generative Neural Video Compression via Video Diffusion Prior [33.164111717707414]
  DiT-based generative neural video compression framework built upon advanced video generation foundation model.<n>First DiT-based generative neural video compression framework built upon advanced video generation foundation model.
  arXiv  Detail & Related papers  (2025-12-04T17:27:32Z)
• Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation [69.57572900337176]
  We introduce Reward Forcing, a novel framework for efficient streaming video generation.<n> EMA-Sink tokens capture both long-term context and recent dynamics, preventing initial frame copying.<n>Re-DMD biases the model's output distribution toward high-reward regions by prioritizing samples with greater dynamics rated by a vision-language model.
  arXiv  Detail & Related papers  (2025-12-04T11:12:13Z)
• Towards One-step Causal Video Generation via Adversarial Self-Distillation [71.30373662465648]
  Recent hybrid video generation models combine autoregressive temporal dynamics with diffusion-based spatial denoising.<n>Our framework produces a single distilled model that flexibly supports multiple inference-step settings.
  arXiv  Detail & Related papers  (2025-11-03T10:12:47Z)
• BADiff: Bandwidth Adaptive Diffusion Model [55.10134744772338]
  Traditional diffusion models produce high-fidelity images by performing a fixed number of denoising steps, regardless of downstream transmission limitations.<n>In practical cloud-to-device scenarios, limited bandwidth often necessitates heavy compression, leading to loss of fine textures and wasted computation.<n>We introduce a joint end-to-end training strategy where the diffusion model is conditioned on a target quality level derived from the available bandwidth.
  arXiv  Detail & Related papers  (2025-10-24T11:50:03Z)
• DiTVR: Zero-Shot Diffusion Transformer for Video Restoration [48.97196894658511]
  DiTVR is a zero shot video restoration framework that couples a diffusion transformer with trajectory aware attention and a flow consistent sampler.<n>Our attention mechanism aligns tokens along optical flow trajectories, with particular emphasis on vital layers that exhibit the highest sensitivity to temporal dynamics.<n>The flow guided sampler injects data consistency only into low-frequency bands, preserving high frequency priors while accelerating cache.
  arXiv  Detail & Related papers  (2025-08-11T09:54:45Z)
• ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos [41.45750971432533]
  Video diffusion models (VDMs) facilitate the generation of high-quality videos.<n>Recent studies have uncovered the existence of "golden noises" that can enhance video quality during generation.<n>We propose ScalingNoise, a plug-and-play inference-time search strategy that identifies golden initial noises for the diffusion sampling process.
  arXiv  Detail & Related papers  (2025-03-20T17:54:37Z)
• Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion [116.40704026922671]
  First-in-first-out (FIFO) video diffusion, built on a pre-trained text-to-video model, has recently emerged as an effective approach for tuning-free long video generation.<n>We propose Ouroboros-Diffusion, a novel video denoising framework designed to enhance structural and content (subject) consistency.
  arXiv  Detail & Related papers  (2025-01-15T18:59:15Z)
• Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Latent Generation [36.098738197088124]
  This work presents a Diffusion Reuse MOtion network to accelerate latent video generation. coarse-grained noises in earlier denoising steps have demonstrated high motion consistency across consecutive video frames. Dr. Mo propagates those coarse-grained noises onto the next frame by incorporating carefully designed, lightweight inter-frame motions.
  arXiv  Detail & Related papers  (2024-09-19T07:50:34Z)
• VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation [88.49030739715701]
  This work presents a decomposed diffusion process via resolving the per-frame noise into a base noise that is shared among all frames and a residual noise that varies along the time axis. Experiments on various datasets confirm that our approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based alternatives in high-quality video generation.
  arXiv  Detail & Related papers  (2023-03-15T02:16:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.