AI Video Papers2026
Automates evaluation of visual world models via a hierarchical agent pipeline that decomposes each case, spawns specialized sub-agents to collect diagnostic evidence, and outputs a verifiable evidence tree plus a final verdict; validated on 18 models across 330 cases and released as a live evaluation pipeline.
AI Video2026
Upscales Minimax H3 24-channel VAE latents in-place to increase spatial resolution while preserving the time dimension. Replaces the decode→pixel-upscale→encode round-trip with a learned 2D/3D latent upscaler to save compute and avoid interpolation ghosting; supports 1.0–4.0× scaling.
AI Video Papers2026
Introduces SemComp-Bench: a benchmark and VLM-based evaluation protocol for measuring outcome achievement and task-relevant semantic grounding in instruction-driven video generation. Ships with SemComp-Data, curated image–instruction–outcome triplets and OA/GR scoring.
AI Video Papers2026
Evaluates visual reasoning in video generation models using 27 photorealistic tasks (810 instances), a two-level taxonomy of domains and skill tags, and task designs that enforce valid intermediate trajectories and calibrated difficulty.
AI Video Papers2026
Yudong Jin, Tao Xie +7Affiliation: State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China, Affiliation: Robbyant, Hangzhou, China +4
Turns an uncalibrated monocular actor video into multiview-consistent novel-view videos and lifts them into 4D Gaussian Splatting assets. Introduces Reference Context Packing to keep reference conditioning fixed-size and Target Context Routing to exchange context across target groups, improving large-view reconstruction consistency.
AI Dataset2026
Provides 1,021.64 hours across 597 CAD/BIM workflows with synchronized screen recordings and interaction logs; each workflow includes video, timestamped input events, task specs, source files, final outputs, and evaluation rubrics for training or evaluating desktop CAD agents.
AI Video Papers2026
Continues a live or ongoing video stream while applying user-specified edits on the fly using a lightweight edit-ignition adapter. The adapter injects edits only in chunks where requests arrive and uses history cross-attention and temporal causal self-attention to preserve continuity and stability for unbounded streaming edits.
AI Video Papers2026
Xianyun Sun, Chaoyou Fu +7Affiliation: Project Leader & Corresponding AuthorProject Page: https://xianyunsun.github.io/OmniAssistBench/, Affiliation: Nankai University +1
Benchmarks assistant-style, multi-turn interaction for omni-modal LLMs on real-time video by reverse-engineering Internet clips into guided multi-turn interactions. It provides predefined priors and segment-level constraints so models must follow exact routes while being evaluated on answer correctness, timing, visual-prompt handling, and context retention.
AI Video Papers2026
Generates enterable omnimodal world-model rollouts that follow continuous 6-DoF camera control while jointly producing 720p video, environmental sound, music and speech. Uses dataset-level motion calibration, a specialized data engine, progressive training and autoregressive post-training to support long-horizon first- and third-person interaction.
AI Video2026
Conditions a MiniMax‑H3 video generator with a single ControlNet‑Union checkpoint to accept Canny, Depth, HED, MLSD or Pose control videos and run video inpainting. Guidance‑distilled for one‑pass inference; requires the base MiniMax‑H3 weights and specific control-branch config.
AI Video2026
Provides Parallel Decoding Distillation (PDD) LoRA adapters that accelerate MiniMax-H3 video generation into few inference steps. Includes official 8-step Acc LoRAs for FL2VA and Ref2VA (rank=64, network_alpha=64, BF16), demo comparison videos, and example scripts using Diffusers' MiniMax-H3 ModularPipeline.
AI Video Papers2026
Measures whether video generators reproduce the correct distribution of possible physical behaviors under repeated rollouts. Introduces PAWBench and PAWEval to convert repeated generations into outcome-level empirical distributions and quantify probabilistic alignment; evaluates 50 scenarios and 11 models and finds no model consistently matches reference probabilities.