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39 件Prediction errors from video world models are often treated as physical surprise, but they can also reflect ap
Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation
Dense 3D tracking from monocular video requires establishing point correspondences over time, estimating their
Existing sketch generation methods often rely on image guidance and are largely limited to individual objects
Video generation is shifting from end-to-end synthesis toward agentic generation, where planning, tool use, an
Multimodal large language models can recognize visual content yet struggle to reason reliably over it across m
Predicting future physical states stands as a central challenge in embodied intelligence. Existing video world
Vision-language models can get individual judgments right while struggling to recover and consistently use the
A capable voice assistant must do more than recognize words; it must understand requests, reason over context,
Recent advances in video world models have enabled the generation of increasingly realistic and interactive vi
Vision-Language-Action (VLA) models have demonstrated promising capabilities in robotic manipulation by integr
An observer sees only part of the surrounding world, yet must keep track of what lies beyond the current view.
Achieving the broad task generalization of embodied models in physical environments remains a central challeng
Training acceleration methods for diffusion transformers, such as REPA and dispersive regularization, are comm
Interactive world models generate visual content conditioned on user actions, yet maintaining spatial consiste
The hair of stylized characters in games and avatars is built from hair cards: artist-placed ribbons whose geo
One-step and few-step text-to-image generation must accurately render the details specified in the prompt desp
We present Youtu-Parsing-Omni, an omni-modal parser that converts documents, images, audio, and video into str
Agentic image editors decompose complex requests into steps, but when a step fails, they mostly retry with the
While frontier vision-language models demonstrate strong capabilities in user interface (UI) navigation, their
We present PhysInsert, a framework for physically plausible reference-based object insertion that jointly adap
Autoregressive (AR) image generators are powerful in principle but inflexible in practice: training with a fix
Humans routinely consume multimodal content, where textual and visual information is naturally interleaved. Al
Unified conditional image generation requires a single model to preserve different aspects of a visual input w
Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as phys
Diffusion vision-language models (DVLMs) process their entire input at every denoising step. Since visual toke
Visual generation requires both high-level semantic knowledge and fine-grained pixel details. We propose a lea
Direct-policy World Action Models (WAM) have demonstrated the benefits of decoupling action generation from ex
Evaluating human-AI co-design requires testing whether coding agents can translate real users' multimodal requ
Generating videos of a specific subject from a few reference images remains challenging when the camera reveal
Extending Vision-Language-Action models (VLAs) to humanoid robots requires coordinated whole-body control and
GUI grounding requires locating small controls in cluttered screenshots and identifying which control a natura
Locating relevant evidence in long inputs is a core capability of Multimodal Large Language Models. Prior stud
Streaming audio-visual target speaker extraction (AVTSE) is essential for latency-sensitive applications. Howe
Standard sequence models encode position by token rank rather than physical coordinate, typically forcing prac
Low-light image enhancement (LLIE) aims to recover well-exposed and perceptually faithful images from severely
Large language model agents have enabled software development through natural-language interaction, but extend
Face image quality assessment (FIQA) estimates the utility of a face image for automated face recognition and
We introduce SIMIT, a test-time self-improvement framework in which a vision-language model creates its own qu