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12 frames- 279NewSynthetic Realities: Evaluating Human Ability to Distinguish AIGenerated Videos from Real Footage
Recent advances in generative AI have enabled multiple pathways for high-fidelity video synthesis: text-to-video, image-to-video animation, and video outpainting. Empirical side-by-side evaluations of how untrained viewers perceive and distinguish these outputs from real footage…
arXiv: video generation - 278NewLearning-to-Memorize: Dynamic Context Management for Long-Horizon Autoregressive Video Generation
Autoregressive video generation has achieved impressive results on short clips, yet generating long videos remains challenging due to error accumulation over extended horizons, where each predicted frame depends on potentially imperfect previous frames. Existing approaches…
arXiv: video generation - 277NewRIFT: Residual-Informed Flow Transfer for One-Step Generative Video Compression
Recent advances in video generation have enabled generative video compression to reconstruct details lost during coding using pretrained spatiotemporal priors. However, a substantial gap remains between codec-induced degradation and the perturbation trajectories learned by…
arXiv: video generation - 275NewRestoring Semantic Fidelity for Training-Free High-Resolution Image Generation in Diffusion Transformers
Diffusion Transformer (DiT)-based text-to-image models have recently achieved remarkable success in image synthesis. However, when directly applied beyond their training resolution, these models often suffer from severe semantic degradation, where meaningful semantic content…
arXiv: image generation - 274NewMedNet and U$^2$-ViT: A Nested Vision Transformer Diffusion Framework for Diverse Medical Images Generation
The scarcity of diverse, high-quality medical images remains a significant bottleneck in advancing AI-driven medical diagnostics and treatment planning. Traditional datasets often suffer from class imbalances, limited disease coverage, and insufficient variations in imaging…
arXiv: image generation
- 273NewRecoverability-Aware Evaluation of Diffusion Video Inpainting and Object Removal
Video inpainting for object removal is scored over the whole hole, although the background behind the object is visible in other frames of the shot for some hole pixels and in no frame for others. A whole-hole score therefore mixes copying with generation and cannot show which…
arXiv: video generation - 272NewIMPACT: Image-Conditioned Motion with Physics-Aware Collision and Trajectory Guidance for Video Generation
Visually realistic video generation does not guarantee physically consistent motion. Generated objects may exhibit implausible collision responses or rebounds without energy dissipation, while even physically valid motion predictions may not be faithfully realized during…
arXiv: video generation - 268NewWhat Helps Denoising Can Hurt Memory: Rethinking History in Long Video Generation
Autoregressive video generators produce long videos block by block, using historical context to maintain temporal consistency. After denoising, a committing pass encodes each clean block into keys and values (KV) that later blocks read, and the same clean block can be encoded…
arXiv: video generation - 266NewWave Forcing: Towards Speed-of-Light Streaming Video Generation
Real-time long-video generation requires continuous output with consistent appearance and natural motion. Existing chunk-wise autoregressive methods face challenges in both generation efficiency and long-video quality: serialized denoising and history-cache refresh require two…
arXiv: video generation - 265NewRunning a 20B Video Diffusion Transformer on Consumer Hardware: A Deployment and Measurement Report for MiniMax H3 on Apple Silicon
We report the end-to-end deployment and measurement of MiniMax H3, a 20.38-billion-parameter video diffusion transformer (DiT) - the count measured from the safetensors headers of the Q8 checkpoint as deployed locally (Section 3.2) - running entirely on consumer Apple Silicon…
arXiv: video generation
- 264NewUniCaMo: Unified Camera and Motion Control for Video Diffusion Models
We present \textbf{UniCaMo}, an unified framework for joint control of camera and object motion in image-and-text-to-video diffusion models. Given a reference image, text prompt, sparse 3D object trajectories, and a 3D camera trajectory, UniCaMo constructs motion-consistent…
arXiv: video generation - 263NewTraining-free Conditional Object Composition for Multi-object Image Generation with Diffusion Models
Multi-object image generation in text-to-image diffusion models remains challenging, as existing approaches struggle to reliably generate all objects specified in a prompt. Compositional approaches that generate objects independently often fail to capture the interactions among…
arXiv: image generation