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Jiang Bian

12 papers hereh-index 212.4k citations33 works total

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

author position
  • middle author4
  • last author7

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.AI2
  • cs.CL2
  • cs.SD2
  • eess.AS2
same name
  • Jiang Bian — 26 papers, h 36
  • Jiang Bian — 18 papers, h 8
  • Jiang Bian — 16 papers, h 10
  • Jiang Bian — 15 papers, h 6
  • Jiang Bian — 14 papers, h 6
  • Jiang Bian — 14 papers, h 7

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 citedNaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models

20 citations · 24 across the 6 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024

Make Your Actor Talk: Generalizable and High-Fidelity Lip Sync with Motion and Appearance Disentanglement

Runyi Yu, Tianyu He, Ailing Zhang +6

We aim to edit the lip movements in talking video according to the given speech while preserving the personal identity and visual details. The task can be decomposed into two sub-p…

cs.CV2024

InstructAvatar: Text-Guided Emotion and Motion Control for Avatar Generation

Yuchi Wang, Junliang Guo, Jianhong Bai +5

Recent talking avatar generation models have made strides in achieving realistic and accurate lip synchronization with the audio, but often fall short in controlling and conveying…

cs.CV2024★ 2 cited

UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing

Jianhong Bai, Tianyu He, Yuchi Wang +4

Recent advances in text-guided video editing have showcased promising results in appearance editing (e.g., stylization). However, video motion editing in the temporal dimension (e.…

cs.CV2023

GAIA: Zero-shot Talking Avatar Generation

Tianyu He, Junliang Guo, Runyi Yu +10

Zero-shot talking avatar generation aims at synthesizing natural talking videos from speech and a single portrait image. Previous methods have relied on domain-specific heuristics…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.