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Sol-Attn introduces a training-free method to sparsify attention for video generation inference, achieving over 2x speedup by dynamically selecting key-value blocks during online softmax with minimal quality loss.
FilmBench is a new benchmark for cinematic video generation, using prompts derived from award-winning films across 20 genres and a three-level cinematic taxonomy with 35+ sub-metrics. It includes an open-source automatic evaluation agent (FilmOps) that reproduces human model rankings with high correlation, revealing significant gaps in dynamic aesthetics and multi-shot performance compared to prior web-style benchmarks.
NVIDIA Research introduces SANA-Video 2.0, a hybrid video diffusion transformer that generates high-quality 720p video on a single GPU, achieving up to 120× speedup over Wan 2.2-14B via hybrid linear-softmax attention and block attention residuals.
A GitHub repository video-shotcraft provides 106 shot recipes, 162 styles, and 161 dynamic previews organized as Agent Skills for AI video generation using Remotion and compatible with Claude Code and Codex.
LTX-Video is an open-source Python repository by Lightricks for generating and conditioning videos locally using LTX-Video models, with support for text/image inputs, multi-condition workflows, and integration with ComfyUI and Diffusers.
ComfyUI is an open-source, node-based AI creation engine that lets builders and visual professionals design complex generation workflows for image, video, audio, and 3D without coding, with partial re-execution and broad model support.
Black Forest Lab's Flux 3 is a new omni-modal AI model capable of generating and predicting images, video, audio, and actions.
BFL has introduced FLUX 3, a multi-modal AI model capable of generating images, videos, and audio.
seedance2 is an AI model that converts depth-video inputs into full video outputs.
This paper introduces a training-free method to improve revisit consistency in autoregressive generative rendering by using temporal and spatial correspondences from the 3D engine to maintain consistent appearance when the camera revisits locations.
SANA-Video 2.0 introduces a hybrid linear-softmax attention mechanism for video diffusion transformers, achieving high-quality video generation up to 720p on a single GPU with significantly reduced latency compared to full-softmax models, while maintaining competitive VBench scores.
GraphVid introduces a graph-conditioned image-to-video generation model that enables interactive control through structured interaction graphs, outperforming prior methods with significant reductions in FID and FVD.
AlayaWorld is a full-stack, open-source video world model capable of generating 720p, 24 FPS streaming video with camera control, enabling dynamic scene creation.
Proposes Self Gradient Forcing (SGF), a two-pass training strategy for autoregressive video diffusion models that provides missing supervision for writing useful context memory, enabling strong long-video extrapolation even from short training windows.
Japan's AIdea Labs released AnimeGen, a free AI model for anime-style video generation, capable of text-to-video and image-to-video, with commercial use allowed.
ShotPlan introduces learnable planning tokens with fractional temporal rotary position embeddings for cinematic multi-shot video generation, enabling explicit shot-level planning and achieving superior inter-shot consistency.
HOMIE is a framework for human-object centric video personalization that integrates MLLM features to improve subject fidelity and interaction patterns, achieving state-of-the-art performance.
HarmoHOI is a unified diffusion framework that jointly generates synchronized multi-view hand-object interaction videos and globally aligned 3D point tracks, achieving state-of-the-art performance in visual quality, motion plausibility, and multi-view geometric consistency.
FVAttn is a training-free sparse attention system that uses runtime load balancing to improve distributed execution efficiency of adaptive sparse attention under multi-GPU sequence parallelism for video generation, achieving up to 4.41x attention speedup and 2.11x inference speedup over FlashAttention on step-distilled Wan2.2 I2V while maintaining competitive video quality.
Apple-π is a benchmark that evaluates video generation models on their ability to reason about physical laws through a three-stage protocol: perception, formulation, deduction. It includes 400 videos covering classical mechanics tasks and reveals current models fall short of reliable law-grounded world simulation.