Reconstructs 4D dynamic human scenes from sparse, low-overlap multi-camera captures by decoupling background synthesis and human modeling. Synthesizes hundreds of camera-controlled background views with a video diffusion model, initializes deformable Gaussian humans via cross-view identity and triangulated keypoints, then applies motion-adaptive recursive enhancement to reduce artifacts.
AI Video2026
Generates minute-scale, temporally coherent dance videos from full music tracks using a hierarchical two-stage approach: global keyframe planning plus local temporal refinement; suitable when long-range musical structure and rhythmic continuity matter.
AI Video2026
Generates a new camera viewpoint from a reference video: an IC‑LoRA adapter for LTX‑Video 2.3 that re‑renders the same scene from a requested discrete camera angle while preserving subject and content. Trained on synthetic multi‑view data, proof‑of‑concept with limited viewpoint range and best for small, chained angle shifts.
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 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.