64 citations · 79 across the 6 of their papers we have counts for
7 papers
Neural Introspection Gating for Adaptive KV-Cache Reuse in Vision-Language-Action Models
Zhijie Wu, Kento Kawaharazuka, Kei Okada
Vision-Language-Action(VLA) models map camera images and language instructions directly to motor commands through a single autoregressive transformer. In real-time control, they st…
MERG3R: A Divide-and-Conquer Approach to Large-Scale Neural Visual Geometry
Leo Kaixuan Cheng, Abdus Shaikh, Ruofan Liang +3
Recent advancements in neural visual geometry, including transformer-based models such as VGGT and Pi3, have achieved impressive accuracy on 3D reconstruction tasks. However, their…
Representing Animatable Avatar via Factorized Neural Fields
Chunjin Song, Zhijie Wu, Bastian Wandt +2
For reconstructing high-fidelity human 3D models from monocular videos, it is crucial to maintain consistent large-scale body shapes along with finely matched subtle wrinkles. This…
Neural Fourier Filter Bank
Zhijie Wu, Yuhe Jin, Kwang Moo Yi
We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, we learn a neural field that decompose the signal both spatially and frequ…
ETNet: Error Transition Network for Arbitrary Style Transfer
Chunjin Song, Zhijie Wu, Yang Zhou +2
Numerous valuable efforts have been devoted to achieving arbitrary style transfer since the seminal work of Gatys et al. However, existing state-of-the-art approaches often generat…
EFANet: Exchangeable Feature Alignment Network for Arbitrary Style Transfer
Zhijie Wu, Chunjin Song, Yang Zhou +2
Style transfer has been an important topic both in computer vision and graphics. Since the seminal work of Gatys et al. first demonstrates the power of stylization through optimiza…
SAGNet:Structure-aware Generative Network for 3D-Shape Modeling
Zhijie Wu, Xiang Wang, Di Lin +3
We present SAGNet, a structure-aware generative model for 3D shapes. Given a set of segmented objects of a certain class, the geometry of their parts and the pairwise relationships…