Research Papers Topics
Models that synthesize video from text or other conditions, including streaming and autoregressive diffusion approaches.
Vision Foundation Models · Zhejiang University
A frozen-feature probe pits VLMs against video-generation models on spatial tasks. VLMs win semantics (92.08 mAP vs 69.89), video models win geometry (0.527 camera AUC vs 0.330), and a naive concat of the two beats both.
WorldCraft lets you click an object and drag its path inside a camera-controlled video world model. It hits 38.90px trajectory error vs DragAnything's 39.86 and keeps camera RPE at 0.131 via world-space paths and a LoRA.
ByG trains image and video editing models with no paired data and no reward model, winning 75.3% of head-to-head video votes against Ditto, which was supervised on millions of pairs.
Video Generation · Peking University
LoomVideo runs text-to-video, editing, and multi-image-to-video in one 5B model, matching 13B baselines on VBench (63.15 vs 63.01) and editing 5.41x faster by adding the source latent instead of concatenating it.
First autoregressive-diffusion lip-sync method: distills a 14B bidirectional teacher into causal 1.3B/14B students that generate each chunk in 2 steps, hitting 31.58 FPS with sub-millisecond time-to-first-frame.
SCAIL-2 feeds the raw driving video into the generation sequence instead of a pose skeleton, cutting FVD to 287 vs 305 for Wan-Animate on Studio-Bench, with one model covering animation and replacement.
Video Generation · Tsinghua University
OmniDirector copies camera motion from a reference video into new generations and cuts rotation error to 2.64 degrees, beating CamCloneMaster (4.11), with no cross-paired training data.
Multimodal Models · Nanjing University
HYDRA-X unifies image and video tokenization in one ViT; tubelet attention and hierarchical temporal patchify improve DAVIS rFVD to 11.19 and editing overall to 4.34.
World Models · Alibaba Qwen Team
ABot-Earth 0.5 uses satellite imagery to generate 3D Gaussian Splatting city scenes, reporting under 10 minutes per square kilometer and FID 16.1.
Video Generation · Nanjing University
CoVEBench: Can Video Editors Follow Complex Instructions? turns complex instruction following for video editing into a checkable test, with concrete failure signals, benchmark limits, and builder takeaways.
World Models · JD.com (Joy Future Academy)
When a camera revisits an old spot, block-wise state-space recurrence scored 69.0 open-domain VLM consistency vs 12.25 for the no-memory baseline; aggressive compression and spatial summaries mostly collapsed.
Multimodal Models · Independent Researcher
VideoKR: Knowledge-Intensive Video Understanding turns knowledge and reasoning in video understanding into a checkable test, with concrete failure signals, benchmark limits, and builder takeaways.
Video Generation · Kuaishou Technology
Instead of asking a video model to reason directly, a VLM grades its in-progress frames and fine-tunes a per-instance LoRA. The trick lifts RULER-Bench from 46.4 to 68.2.
Echo-Infinity is an autoregressive video model with a learnable evolving memory that compresses any-length history at constant cost, hitting 24-hour rollouts (over 1.3M frames) in real time at 18.5 FPS on an H100.
SANA-Streaming edits 1280x704 video in real time at 24 end-to-end FPS on a single RTX 5090, with the diffusion transformer core hitting 58 FPS via a hybrid DiT and Cycle-Reverse Regularization.
Video Generation · Virginia Tech
VideoMLA ports Multi-Head Latent Attention into causal video diffusion, cutting per-token KV memory 92.7% (224 vs 3,072 scalars), winning VBench at 60s, and lifting B200 throughput 1.23x.
Diffusion Models · University of Science and Technology of China
Stream-R1 reweights DMD losses by video reward scores and per-region perplexity instead of treating signals equally. Its 1.3B streaming model hits 84.40 VBench at 23.1 FPS, beating its 14B teacher's 84.26 for free.