arXiv Computer Vision
Real-Time Joint Audio-Video Generation by Parallel Adapter Composition
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 10
Decoupled Self-Forcing Distillation for Streaming Talking Head Generation
arXiv:2609.10317v1 Announce Type: new Abstract: Streaming talking-head generation produces each frame as its driving audio arrives, yet fidelity and efficiency have so far pulled in opposite directio...
Hugging Face Trending Papers
Aug 6
Vorch-Streamer: Extending Human Audio-Visual Generation to Real-Time Long-Form Streaming
Real-time long-form avatar audio--video generation requires causal, continuous synthesis while maintaining audiovisual synchronization and visual consistency. Adapting a pretrained bidirectional model to this setting presents two key dilemmas.
arXiv Computer Vision
Sep 1
DreamX-Creator: Democratizing Native Audio-Video Generation at 2K Resolution
arXiv:2608.31106v1 Announce Type: new Abstract: Recent video generators often omit audio or synthesize it in a separate stage, limiting reciprocal modeling of visual dynamics and acoustic events. We...
arXiv Computer Vision
Sep 7
Encore: Infinite Audio-Video Generation with Adaptive Signal Routing
Encore is a new framework for generating long, synchronized audio‑video content. It splits the problem into local continuity, handled by iterative chunk‑wise synthesis with cross‑chunk context, and global consistency, enforced through reference audio‑video signals with shifted position embeddings. The Adaptive Signal Routing mechanism learns attention biases and residual scales to modulate the influence of each conditioning signal, enabling end‑to‑end joint audio‑video generation and infinite‑length inference.
arXiv Computer Vision
Aug 24
Instruction-Based Video Editing by Repurposing an Image Editing Model
Instruction-Based Video Editing by Repurposing an Image Editing Model demonstrates that a strong image‑editing model can be adapted to edit videos by operating on video‑VAE latents. The authors tile latent frames into a large virtual image, reuse the editor’s positional encoding, and bridge latent spaces with lightweight projections, fine‑tuning on Ditto‑1M editing triplets. Their experiments show that per‑frame video latents are close enough to the image domain that mature image‑editing priors transfer with minimal adaptation.
arXiv Computer Vision
Sep 25
ViRDM: Taming Representation Distribution Matching for Few-Step Causal Video Generation
ViRDM is a new post‑training method for few‑step causal video generation that eliminates the need for a large teacher model and an online critic. By applying representation distribution matching (RDM) with a precomputed target distribution, a lightweight VAE decoder, and staged vector–Jacobian products, ViRDM overcomes memory, optimization, and temporal dynamics challenges. The approach reduces GPU memory usage and training time, achieving state‑of‑the‑art VBench performance with only 20 generator updates and 16 A100 GPU‑hours.