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20222024
most citedLearning to Grow Pretrained Models for Efficient Transformer Training

13 citations · 22 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.CV20241 cited

Taming Mode Collapse in Score Distillation for Text-to-3D Generation

Peihao Wang, Dejia Xu, Zhiwen Fan +8

Despite the remarkable performance of score distillation in text-to-3D generation, such techniques notoriously suffer from view inconsistency issues, also known as "Janus" artifact…

cs.CV20243 cited

SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity

Peihao Wang, Zhiwen Fan, Dejia Xu +8

Score distillation has emerged as one of the most prevalent approaches for text-to-3D asset synthesis. Essentially, score distillation updates 3D parameters by lifting and back-pro…

cs.CV2023

Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts

Wenyan Cong, Hanxue Liang, Peihao Wang +5

Cross-scene generalizable NeRF models, which can directly synthesize novel views of unseen scenes, have become a new spotlight of the NeRF field. Several existing attempts rely on…

cs.CV2022

Neural Implicit Dictionary via Mixture-of-Expert Training

Peihao Wang, Zhiwen Fan, Tianlong Chen +1

Representing visual signals by coordinate-based deep fully-connected networks has been shown advantageous in fitting complex details and solving inverse problems than discrete grid…

cs.CV20223 cited

Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations

Tianlong Chen, Peihao Wang, Zhiwen Fan +1

Neural Radiance Field (NeRF) regresses a neural parameterized scene by differentially rendering multi-view images with ground-truth supervision. However, when interpolating novel v…