All modalities are equal, but video is more equal: Closing the Cross-Attention Gap in Joint Video Generation

arXiv:2609.27901 · cs.CV · Submitted 2026-09-23 · Read on arXiv

cs.CV

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Alignment — 15× in this paper · explained in 1 episode
• Fine-Tuning — 14× in this paper · explained in 5 episodes
• Consistency — 12× in this paper · explained in 3 episodes
• Diffusion Model — 11× in this paper · explained in 8 episodes
• Diffusion Models — 11× in this paper · explained in 6 episodes
• Regularization — 10× in this paper · explained in 3 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 5× in this paper
• Adaptation — 4× in this paper · explained in 1 episode
• Fidelity — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 3× in this paper
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Semantic Alignment — 3× in this paper · explained in 3 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Multimodal Learning — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• LoRA Adaptation — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• LTX-2: Efficient Joint Audio-Visual Foundation Model
• Prompt-to-Prompt Image Editing with Cross Attention Control
• JoVA: Unified Multimodal Learning for Joint Video-Audio Generation and Editing
• Taming Visually Guided Sound Generation
• Inference-Time Scaling for Joint Audio-Video Generation
• Towards 3D-Aware Video Diffusion Models: Render-Free Human Motion Control with Mesh Tokenization
• T2AV-Compass: Towards Unified Evaluation for Text-to-Audio-Video Generation
• VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
• JUST-DUB-IT: Video Dubbing via Joint Audio-Visual Diffusion
• SkyReels-V4: Multi-modal Video-Audio Generation, Inpainting and Editing model
• IDCNet: Guided Video Diffusion for Metric-Consistent RGBD Scene Generation with Precise Camera Control
• Ovi: Twin Backbone Cross-Modal Fusion for Audio-Video Generation
• MediaPipe: A Framework for Building Perception Pipelines
• Improving Joint Audio-Video Generation with Cross-Modal Context Learning
• Generating Human Motion Videos using a Cascaded Text-to-Video Framework
• Bringing Objects to Life: training-free 4D generation from 3D objects through view consistent noise
• OmniForcing: Unleashing Real-time Joint Audio-Visual Generation
• ProAV-DiT: A Projected Latent Diffusion Transformer for Efficient Synchronized Audio-Video Generation
• MOVA: Towards Scalable and Synchronized Video-Audio Generation
• UniVerse-1: Unified Audio-Video Generation via Stitching of Experts

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models