AI Model2026
Timestamp-aware realtime video→text model that processes incoming frames continuously, answers questions mid-stream or emits silence when evidence is insufficient, and can revise earlier outputs as new frames arrive. Built for timestamped multimodal interaction with a 256K context and an 11B-parameter backbone.
AI Video Papers2026
Comprehensive benchmark and automated evaluation framework for keyframe-conditioned video generation—decomposes keyframe execution into six metrics and assesses overall video quality with evidence-grounded MLLM judgments and specialized perception models.
AI Video Papers2026
Enables efficient, generalist video understanding by combining an Inflated 3D Vision Transformer and adaptive frame-resolution streaming with a scalable video data synthesis pipeline; ships as a fully open 4B-parameter MLLM that improves general, long-form, and streaming benchmarks.
AI Video Papers2026
Evaluates whether video models reason according to physical laws by treating generated videos as visible reasoning traces and using a three-stage Perception–Formulation–Deduction protocol. Includes Orchard (400 mechanics videos), chain-of-frames prompting on annotated first frames, and a hybrid MLLM-plus-objective scoring suite for stage-resolved diagnostics.
AI Video Papers2026
Predicts variable-cardinality sets of evidence intervals in videos to temporally ground queries using multimodal large language models. Combines caption-derived multi-span supervision, a temporal Wasserstein matching-free reward, and temporal IoU, yielding strong mIoU gains across multiple benchmarks.
AI Dataset2026
Provides 2,000 hours of synchronized, high‑fidelity robot‑free bimanual manipulation demonstrations with multi‑view video, calibrated end‑effector trajectories, gripper states, and language annotations. Curated from a 20,000+ hour corpus; features 6 camera views, ~3 mm pose accuracy, <40 µs cross‑sensor sync, and LeRobot v3‑style Parquet+MP4 export under CC BY 4.0.
AI Video2026
Turns Claude Code or Codex into an agent skill that storyboards, animates, and sound-designs cinematic product promos from your real screenshots using Remotion. Packs tuned shot recipe cards, 150+ motion previews, a production-ready template, Remotion components, and audio assets for web/desktop product films.
AI Video Papers2026
Personalizes subject-driven videos to preserve human identity and accurate human–object interactions by integrating multimodal references and MLLM-derived semantics. Introduces global multimodal guidance in self-attention and modality-reference embeddings to align MLLM features with VAE tokens, supporting both inter- and intra-subject inputs (e.g., OCR, multi-view).
AI Video Papers2026
Generates interactive long-horizon 24-fps video worlds (540p/720p) from text, image, or video inputs. Uses a 15B video diffusion transformer with a bounded visual context (sink frame, compressed temporal history, geometry-aligned spatial memory, recent-frame conditioning) and a discrete autoregressive distillation that cuts inference to ~4 sampling steps per chunk.
AI Model2026
Generates and reasons about multimodal physical-world content—text, images, video and action trajectories—conditioned on text, images, video and robot/vehicle action inputs. An edge-sized (4B) Mixture‑of‑Transformers omni-model optimized for single‑GPU inference and Physical AI tasks (image→video, action prediction, robot policy).
Synthesizes RGB frames from structured world states exported by physics engines; it reformulates a heavy generative renderer into a few-step autoregressive streaming model and uses lightweight distilled codecs to reach playable ~30 FPS while preserving G-buffer and prompt control.
AI Video Papers2026
Extrapolates long video sequences from very short contexts by restoring memory-writing supervision in autoregressive video diffusion models using a two-pass Self Gradient Forcing (SGF). SGF records a no-gradient rollout at a sampled denoising exit and then recomputes KV context in a second parallel pass so future losses teach earlier latent writes, enabling minutes-long extrapolation from ~5s windows.