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.
Rewrites physical scenes as executable world programs (e.g., MuJoCo scene descriptions) and uses an agentic abductive loop to propose, execute, render, verify, and iteratively refine those programs from videos or text. Verified executable worlds supply scalable physical supervision for training vision–language models.
AI Dataset2026
Provides a unified, camera-conditioned, multi-source video dataset and reproducible processing pipeline for training long-horizon video world models. Key features: a canonical frame-aligned contract (visuals, camera geometry, captions, quality metadata), 1.43M canonical clips with portable releases and reconstruction tools; large download and some backbone assets carry separate licenses.
AI Video Papers2026
Generates synchronized native 2K audio-video from a single first frame and a text prompt using a compact 7B joint generator. Combines gated cross-modal attention, progressive joint training, audio-video reinforcement learning, and an Autoregressive 1-Step 2K Refinement; releases a 7B generator and 2K Refiner for research use.
AI Video2026
Replaces a character in a video using a single repainted frame from the same clip and propagates that edit across the shot while preserving motion, camera and lighting; requires no pose estimator, segmentation, face tracker or text prompt. Key facts: a 33.1B MiniMax-H3 finetune, DMD-distilled to three forward passes, 124 frames in ~26s on one B200 GPU.
Embodied AI2026
Learns generalizable World Action Models for robotic manipulation by scaling causal egocentric video pretraining and grounding learned dynamics with heterogeneous robot trajectories. Key features: a three-stage curriculum (video pretraining, video-action mid-training with a unified action representation, and target-robot specialization) and a Slow–Fast dual-system for 30 Hz real-time action prediction.
AI Dataset2026
Provides an unattended text-to-video-and-audio streaming toolkit built around FastH3 (a 4-step distillation of MiniMax-H3): generation/retime/HTTP push scripts, a 221-scene prompt library, checkpoint conversion and ComfyUI workflows to run a continuous local stream.
AI Video2026
Adds a plug-and-play linear-attention branch and LoRA adapters to MiniMax-H3 to run text-to-video generation faster than real-time (near-lossless quality tradeoffs). Includes an optimized FP8 inference stack and a community license with regional restrictions.
AI Dataset2026
Provides 4.5 billion TikTok video records with captions, timestamps, music IDs and engagement counts for research; split across 27 zstd-compressed Parquet files (~289 GB) and sampled via TikTok's mobile API; released for research-use only with privacy and ToS caveats.
AI Video Papers2026
Provides an end-to-end, reproducible foundation for camera‑controllable, long‑horizon video world models — converting 1.43M clips from 10 datasets into a unified canonical corpus and releasing data, pipelines, recipes, and weights. Introduces backbone‑native adaptation and a three‑stage training recipe to produce 5B–33B models that enable minute‑to‑hour rollouts after training on 5s sequences.
Trains a world-action model that predicts future visual states to guide zero-shot robotic manipulation; introduces CoAE, SVP, IDM and KASO to pretrain generative and action components from scratch on manipulation data, scaling up to 30,000 hours and improving zero-shot success.
Generates compositional 3D scenes as collections of individual object meshes by conditioning a single-object 3D generative prior on multi-view posed observations. Key features include anchor-aligned canonical frames, multi-view DINOv3 feature lifting with an IBR-style fusion, and LoRA adaptation to complete heavily occluded objects; includes a large UE-MeshyScene benchmark.