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20182026
most citedLocally Attentional SDF Diffusion for Controllable 3D Shape Generation

120 citations · 321 across the 32 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.CV2023★ 2 cited

HumanTOMATO: Text-aligned Whole-body Motion Generation

Shunlin Lu, Ling-Hao Chen, Ailing Zeng +4

This work targets a novel text-driven whole-body motion generation task, which takes a given textual description as input and aims at generating high-quality, diverse, and coherent…

cs.CV2023★ 2 cited

TOSS:High-quality Text-guided Novel View Synthesis from a Single Image

Yukai Shi, Jianan Wang, He Cao +7

In this paper, we present TOSS, which introduces text to the task of novel view synthesis (NVS) from just a single RGB image. While Zero-1-to-3 has demonstrated impressive zero-sho…

cs.CV2023

Reinforced Disentanglement for Face Swapping without Skip Connection

Xiaohang Ren, Xingyu Chen, Pengfei Yao +2

The SOTA face swap models still suffer the problem of either target identity (i.e., shape) being leaked or the target non-identity attributes (i.e., background, hair) failing to be…

cs.CL2023★ 55 cited

Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph

Jiashuo Sun, Chengjin Xu, Lumingyuan Tang +6

Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep an…

cs.CV2023★ 120 cited

Locally Attentional SDF Diffusion for Controllable 3D Shape Generation

Xin-Yang Zheng, Hao Pan, Peng-Shuai Wang +3

Although the recent rapid evolution of 3D generative neural networks greatly improves 3D shape generation, it is still not convenient for ordinary users to create 3D shapes and con…

cs.CV2023★ 1 cited

Closed-Loop Transcription via Convolutional Sparse Coding

Xili Dai, Ke Chen, Shengbang Tong +9

Autoencoding has achieved great empirical success as a framework for learning generative models for natural images. Autoencoders often use generic deep networks as the encoder or d…