REVIEW 6 cited by
VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution. Despite its recent success in image synthesis, applying DPMs to video generation is still challenging due to high-dimensional data spaces. Previous methods usually adopt a standard diffusion process, where frames in the same video clip are destroyed with independent noises, ignoring the content redundancy and temporal correlation. 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. The denoising pipeline employs two jointly-learned networks to match the noise decomposition accordingly. Experiments on various datasets confirm that our approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based alternatives in high-quality video generation. We further show that our decomposed formulation can benefit from pre-trained image diffusion models and well-support text-conditioned video creation.
Forward citations
Cited by 6 Pith papers
-
MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
None of ten tested video-generation models reliably remembers objects after occlusion in dynamic scenes; static-camera videos inflate consistency scores.
-
4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans
A diffusion model trained on 60,000 fitted 4D Gaussian Splatting human clips generates text-prompted, view-consistent dynamic humans directly in 4D, over 10x faster than video-first pipelines.
-
Flex-Forcing: Towards a Unified Autoregressive and Bidirectional Video Diffusion Model
A single video diffusion model, trained with flexible temporal–timestep chunking and noise-aligned keys, supports bidirectional, autoregressive, and hybrid inference with better quality–efficiency than Self-Forcing.
-
Uncertainty-o: One Model-agnostic Framework for Unveiling Uncertainty in Large Multimodal Models
Uncertainty-o estimates uncertainty in large multimodal models by perturbing prompts and computing entropy over semantically clustered answers, improving hallucination detection across five modalities.
-
From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence
Physical intelligence needs an embodied brain that reasons over interventions and emits capability requests, grounded by a physical harness and shared experience contracts rather than direct actuator policies.
-
Next Block Prediction: Video Generation via Semi-Autoregressive Modeling
NBP generates video rows in parallel with block-wise attention, achieving 11x faster inference and better FVD than next-token autoregressive models.
Discussion (0). Continue with ORCID to comment.