Arbitrary-Scale 3D Gaussian Super-Resolution

The paper presents StreamingTalker, an autoregressive diffusion model designed for speech-driven 3D facial animation. This model addresses the limitations of previous methods that process audio sequences in a single pass, which can lead to poor performance with longer inputs and increased latency. By processing audio in a streaming manner, StreamingTalker offers flexibility with varying audio lengths and reduces latency, enhancing the realism and synchronization of facial animations.

This research explores retrieval augmented generation as a method for automatic context discovery in context-aware Automatic Speech Recognition (ASR) systems, aiming to enhance transcription accuracy, especially with rare or out-of-vocabulary terms. The study introduces an embedding-based retrieval approach and evaluates its effectiveness against large language model alternatives. Experiments show a reduction in word error rate (WER) by up to 17% compared to no-context, with oracle context achieving a 24.1% reduction.

RoboTidy is a new benchmark designed for language-guided household tidying, addressing the limitations of current benchmarks that fail to model user preferences and support mobility. It features 500 photorealistic 3D Gaussian Splatting household scenes and provides extensive manipulation and navigation trajectories to facilitate training and evaluation in Vision-Language-Action and Vision-Language-Navigation tasks.

The paper introduces a novel task called code-to-style image generation, which aims to create images with unique and consistent visual styles based solely on numerical style codes. This approach addresses challenges faced by existing generative methods that rely on extensive textual prompts or reference images. The authors present CoTyle, the first open-source method for this task, filling a gap in academic research on visual stylization, which has been largely dominated by industry players like Midjourney.

This paper presents a tractable algorithm for estimating an unknown Lipschitz function from noisy observations, establishing an upper bound on its convergence rate. The approach extends max-affine methods from convex shape-restricted regression to a broader Lipschitz setting. A key component is a nonlinear feature expansion that maps max-affine functions into delta-convex functions, achieving the minimax convergence rate under squared loss and subgaussian distributions.

Large Language Models (LLMs) are advanced linguistic tools that can produce outputs that may sound plausible but are often factually incorrect, a phenomenon known as hallucination. This study introduces a mathematical framework to analyze, quantify, and mitigate these hallucinations. It employs probabilistic modeling and Bayesian uncertainty estimation to develop refined metrics and strategies, including contrastive decoding and retrieval-augmented grounding, aimed at enhancing the reliability of LLMs.

The paper presents a novel physical adversarial attack targeting stereo matching models used in autonomous driving. Unlike traditional attacks that utilize 2D patches, this approach employs a 3D physical adversarial example (PAE) with global camouflage texture, enhancing visual consistency across various viewpoints. Additionally, a new 3D stereo matching rendering module is introduced to align the PAE with real-world positions in binocular vision, addressing the disparity effects of stereo cameras.

Gaussian See, Gaussian Do is a new method for semantic 3D motion transfer from multiview video. This approach allows for rig-free, cross-category motion transfer between objects that have semantically meaningful correspondence. By utilizing implicit motion transfer techniques, the method extracts motion embeddings from source videos and applies them to static target shapes, resulting in improved motion fidelity and structural consistency in 3D Gaussian Splatting reconstruction.