12 citations · 17 across the 8 of their papers we have counts for
9 papers
AnimalLift: Reconstructing Animatable 3D Animals from a Single Image by Learning Canonical Shape, Texture, and Fur Maps
Chunyi Sun, Ruyi Zha, Weijian Deng +3
Reconstructing a fully animatable 3D animal from a single image remains challenging because animation-ready assets require not only plausible geometry, but also a unified topology,…
Confidence and Dispersity as Signals: Unsupervised Model Evaluation and Ranking
Weijian Deng, Weijie Tu, Ibrahim Radwan +3
Assessing model generalization under distribution shift is essential for real-world deployment, particularly when labeled test data is unavailable. This paper presents a unified an…
RefRef: A Dataset and Benchmark for Reconstructing Refractive and Reflective Objects
Yue Yin, Enze Tao, Weijian Deng +1
Modern 3D reconstruction and novel view synthesis approaches have demonstrated strong performance on scenes with opaque, non-refractive objects. However, most assume straight light…
Ranked from Within: Ranking Large Multimodal Models Without Labels
Weijie Tu, Weijian Deng, Dylan Campbell +4
Can the relative performance of a pre-trained large multimodal model (LMM) be predicted without access to labels? As LMMs proliferate, it becomes increasingly important to develop…
Manual-PA: Learning 3D Part Assembly from Instruction Diagrams
Jiahao Zhang, Anoop Cherian, Cristian Rodriguez +2
Assembling furniture amounts to solving the discrete-continuous optimization task of selecting the furniture parts to assemble and estimating their connecting poses in a physically…
Toward a Holistic Evaluation of Robustness in CLIP Models
Weijie Tu, Weijian Deng, Tom Gedeon
Contrastive Language-Image Pre-training (CLIP) models have shown significant potential, particularly in zero-shot classification across diverse distribution shifts. Building on exi…
A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)
Weijie Tu, Weijian Deng, Tom Gedeon
Contrastive Language-Image Pre-training (CLIP) models have demonstrated remarkable generalization capabilities across multiple challenging distribution shifts. However, there is st…
An Empirical Study Into What Matters for Calibrating Vision-Language Models
Weijie Tu, Weijian Deng, Dylan Campbell +2
Vision-Language Models (VLMs) have emerged as the dominant approach for zero-shot recognition, adept at handling diverse scenarios and significant distribution changes. However, th…
3D-GPT: Procedural 3D Modeling with Large Language Models
Chunyi Sun, Junlin Han, Weijian Deng +3
In the pursuit of efficient automated content creation, procedural generation, leveraging modifiable parameters and rule-based systems, emerges as a promising approach. Nonetheless…