Understanding bimanual human hand activities is a critical problem in AI and robotics. We cannot build large models of bimanual activities because existing datasets lack the scale, coverage of diverse hand activities, and detailed annotations. We introduce GigaHands, a massive annotated dataset capturing 34 hours of bimanual hand activities from 56 subjects and 417 objects, totaling 14k motion clips derived from 183 million frames paired with 84k text annotations. Our markerless capture setup and data acquisition protocol enable fully automatic 3D hand and object estimation while minimizing the effort required for text annotation. The scale and diversity of GigaHands enable broad applications, including text-driven action synthesis, hand motion captioning, and dynamic radiance field reconstruction.

Object-goal navigation requires mobile robots to efficiently locate targets with visual and spatial information, yet existing methods struggle with generalization in unseen environments. Heuristic approaches with naive metrics fail in complex layouts, while graph-based and learning-based methods suffer from environmental biases and limited generalization. Although Large Language Models (LLMs) as planners or agents offer a rich knowledge base, they are cost-inefficient and lack targeted historical experience. To address these challenges, we propose the LLM-enhanced Object Affinities Transfer (LOAT) framework, integrating LLM-derived semantics with learning-based approaches to leverage experiential object affinities for better generalization in unseen settings. LOAT employs a dual-module strategy: one module accesses LLMs’ vast knowledge, and the other applies learned object semantic relationships, dynamically fusing these sources based on context. Evaluations in AI2-THOR and Habitat simulators show significant improvements in navigation success and efficiency, and real-world deployment demonstrates the zero-shot ability of LOAT to enhance object-goal navigation systems.

As XR technology continues to advance rapidly, 3D generation and editing are increasingly crucial. Among these, stylization plays a key role in enhancing the appearance of 3D models. By utilizing stylization, users can achieve consistent artistic effects in 3D editing using a single reference style image, making it a user-friendly editing method. However, recent NeRF-based 3D stylization methods encounter efficiency issues that impact the user experience, and their implicit nature limits their ability to accurately transfer geometric pattern styles. Additionally, the ability for artists to apply flexible control over stylized scenes is considered highly desirable to foster an environment conducive to creative exploration. To address the above issues, we introduce StylizedGS, an efficient 3D neural style transfer framework with adaptable control over perceptual factors based on 3D Gaussian Splatting representation. We propose a filter-based refinement to eliminate floaters that affect the stylization effects in the scene reconstruction process. The nearest neighbor-based style loss is introduced to achieve stylization by fine-tuning the geometry and color parameters of 3DGS, while a depth preservation loss with other regularizations is proposed to prevent the tampering of geometry content. Moreover, facilitated by specially designed losses, StylizedGS enables users to control color, stylized scale, and regions during the stylization to possess customization capabilities. Our method achieves high-quality stylization results characterized by faithful brushstrokes and geometric consistency with flexible controls. Extensive experiments across various scenes and styles demonstrate the effectiveness and efficiency of our method concerning both stylization quality and inference speed.

Current 3D-aware facial NeRF generation approaches control the facial appearance by text, lighting conditions or reference images, limiting precise manipulation of local facial regions and interactivity. Color stroke, a user-friendly and effective tool to depict appearance, is challenging to edit 3D faces because of the lack of texture, coarse geometry representation and detailed editing operations. To solve the above problems, we introduce StrokeFaceNeRF, a novel stroke-based method for editing facial NeRF appearance. In order to infer the missing texture and 3D geometry information, 2D edited stroke maps are firstly encoded into the EG3D's latent space, followed by a transformer-based editing module to achieve effective appearance changes while preserving the original geometry in editing regions. Notably, we design a novel geometry loss function to ensure surface density remains consistent during training. To further enhance the local manipulation accuracy, we propose a stereo fusion approach which lifts the 2D mask (inferred from strokes or drawn by users) into 3D mask volume, allowing explicit blending of the original and edited faces. Extensive experiments validate that the proposed method outperforms existing 2D and 3D methods in both editing reality and geometry retention.