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Xiaodan Liang

10 papers hereh-index 4104 citations13 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author8

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CV9
  • cs.AI1
same name
  • Xiaodan Liang — 119 papers, h 77
  • Xiaodan Liang — 47 papers, h 27
  • Xiaodan Liang — 47 papers, h 34
  • Xiaodan Liang — 25 papers, h 8
  • Xiaodan Liang — 23 papers, h 8
  • Xiaodan Liang — 18 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving

2 citations · 2 across the 9 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CV2024

AtomThink: Multimodal Slow Thinking with Atomic Step Reasoning

Kun Xiang, Zhili Liu, Terry Jingchen Zhang +12

In this paper, we address the challenging task of multimodal reasoning by incorporating the notion of ``slow thinking'' into multimodal large language models (MLLMs). Our core idea…

cs.CV2024

Learning Interaction-aware 3D Gaussian Splatting for One-shot Hand Avatars

Xuan Huang, Hanhui Li, Wanquan Liu +4

In this paper, we propose to create animatable avatars for interacting hands with 3D Gaussian Splatting (GS) and single-image inputs. Existing GS-based methods designed for single…

cs.CV2024★ 2 cited

ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving

Jiehui Huang, Xiao Dong, Wenhui Song +9

Diffusion-based technologies have made significant strides, particularly in personalized and customized facialgeneration. However, existing methods face challenges in achieving hig…

cs.CV2024

3D Visibility-aware Generalizable Neural Radiance Fields for Interacting Hands

Xuan Huang, Hanhui Li, Zejun Yang +2

Neural radiance fields (NeRFs) are promising 3D representations for scenes, objects, and humans. However, most existing methods require multi-view inputs and per-scene training, wh…

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