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Tianyi Chu

6 accepted papers

2026

M$^2$-Miner: Multi-Agent Enhanced MCTS for Mobile GUI Agent Data Mining

ICLR 2026poster

Graphical User Interface (GUI) agent is pivotal to advancing intelligent human-computer interaction paradigms. Constructing powerful GUI agents necessitates the large-scale annotation of high-quality user-behavior trajectory data (\textit{i.e.}, intent–trajectory pairs) for training. However, manual…

Cited by 0SourceScholar
2024

Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation

AAAI 2024technical

Existing generative adversarial network (GAN) based conditional image generative models typically produce fixed output for the same conditional input, which is unreasonable for highly subjective tasks, such as large-mask image inpainting or style transfer. On the other hand, GAN-based diverse image…

Cited by 2SourcePDFScholar
2024

PNeSM: Arbitrary 3D Scene Stylization via Prompt-Based Neural Style Mapping

AAAI 2024technical

3D scene stylization refers to transform the appearance of a 3D scene to match a given style image, ensuring that images rendered from different viewpoints exhibit the same style as the given style image, while maintaining the 3D consistency of the stylized scene. Several existing methods have obtai…

Cited by 2SourcePDFScholar
2024

Single-Mask Inpainting for Voxel-based Neural Radiance Fields

ECCV 2024poster

"3D inpainting is a challenging task in computer vision and graphics that aims to remove objects and fill in missing regions with a visually coherent and complete representation of the background. A few methods have been proposed to address this problem, yielding notable results in inpainting. Howev…

Cited by 1SourcePDFScholar
2023

Rethinking Fast Fourier Convolution in Image Inpainting

ICCV 2023poster

Recently proposed image inpainting method LaMa builds its network upon Fast Fourier Convolution (FFC), which was originally proposed for high-level vision tasks like image classification. FFC empowers the fully convolutional network to have a global receptive field in its early layers. Thanks to the…

Cited by 33PDFScholar
2023

TeSTNeRF: Text-Driven 3D Style Transfer via Cross-Modal Learning

IJCAI 2023poster

Text-driven 3D style transfer aims at stylizing a scene according to the text and generating arbitrary novel views with consistency. Simply combining image/video style transfer methods and novel view synthesis methods results in flickering when changing viewpoints, while existing 3D style transfer m…

Cited by 16SourcePDFScholar