Chia-Wen Chen

Chia-Wen Chen, Yan Wu, Korrawe Karunratanakul et al.

Achieving precise, versatile whole-body character control in physics-based animation remains challenging. Recent diffusion-based policies generate rich and expressive motions but typically rely on gradient-based test-time guidance to satisfy task objectives, which is slow and can reduce robustness. We introduce NaP-Control (Navigating Diffusion Prior for Versatile and Fast Character Control), abbreviated as NaP. Our method uses reinforcement learning to manipulate the latent noise of a task-agnostic diffusion policy prior, steering it toward task-specific behaviors for fast, robust control with high motion fidelity. In contrast to methods that rely solely on offline training, NaP interacts with the environment during training to correct motions and optimize task rewards, improving success rates and enabling adaptation to challenging scenarios. By directly predicting task-optimized diffusion noise, NaP eliminates iterative guidance during denoising and enables efficient inference. Experiments show that NaP attains higher success rates and faster inference while preserving natural motion across diverse tasks.

Joe Watson, Ivan O'Conner, Chia-Wen Chen et al. · cambridge

Psychological assessments typically rely on structured rating scales, which cannot incorporate the rich nuance of a respondent's natural language. This study leverages recent LLM advances to harness qualitative data within a novel conceptual framework, combining LLM-scored text and traditional rating-scale items to create an augmented test. We demonstrate this approach using depression as a case study, developing and assessing the framework on a real-world sample of upper secondary students (n=693) and corresponding synthetic dataset (n=3,000). On held-out test sets, augmented tests achieved statistically significant improvements in measurement precision and accuracy. The information gain from the LLM items was equivalent to adding between 6.3 (real data) and 16.0 (synthetic data) items to the original 19-item test. Our approach marks a conceptual shift in automated scoring that bypasses its typical bottlenecks: instead of relying on pre-labelled data or complex expert-created rubrics, we empirically select the most informative LLM scoring instructions based on calculations of item information. This framework provides a scalable approach for leveraging the growing stream of transcribed text to enhance traditional psychometric measures, and we discuss its potential utility in clinical health and beyond.