AI Video Papers2026
Externalizes persistent scene state into a camera-indexed world bank and designs a long-horizon teacher whose sparse-attention supervision is distilled into a three-step student, enabling responsive, low-latency interactive long-horizon video generation with bounded denoiser context.
AI Video Papers2026
Predicts future video frames conditioned on an observed frame, a language instruction, and a sequence of end-effector poses and gripper states for robot manipulation. Uses per-arm SE(3) geometric encoding (PRoPE-style), a lightweight depth branch, SAM3 masks with a frozen V-JEPA teacher, and distribution-matching distillation for efficient, consistent action-conditioned rollouts.
AI Model2026
Provides a 27B Qwen3.8 GGUF build for local/offline deployment, optimized with Unsloth Dynamic V3.0 quantization. Offers switchable thinking-mode, native vision-language understanding, and native long-context support (262k+ tokens).
AI Model2026
A 27B Qwen3.8 vision‑language causal transformer quantized to NVFP4 for lower‑memory inference. Provides 262K native context (extensible to 1M), Unsloth Dynamic V3.0 4‑bit quantization and MTP support so Qwen3.8‑class multimodal workloads can run on 24GB‑class GPUs.
Provides a large-scale benchmark and a human-aligned metric for humanoid whole-body motion tracking — about 153 hours of optical mocap from professional performers plus HumanScore trained on 12K human-labeled preference pairs to reveal contact, timing, and stability failures.
AI Video Papers2026
Systematically evaluates AI-generated video detectors and generators for real-world crisis scenarios using RA-Bench (17,886 clips: 1,830 real anchors, 16,056 generated). Shows detector families fail to generalize across generation conditions, and that human-misleading videos and social dissemination further degrade detection.
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
Yunheng Li, Guohong Mu +5VCIP, School of Computer Science, Nankai University, Brain and Artificial Intelligence Lab, Northwestern Polytechnical University +2
Treats human annotations as oracle rollouts and separates them from on-policy baselines to improve reinforcement learning for video multimodal LLMs. Key features include a decoupled advantage estimator, sign-balanced pruning, and scalable gains across model sizes and data budgets.
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.