18 citations · 43 across the 10 of their papers we have counts for
10 papers
PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with Auto-Regressive Transformer
Jingwen Ye, Yuze He, Yanning Zhou +5
Shape primitive abstraction, which decomposes complex 3D shapes into simple geometric elements, plays a crucial role in human visual cognition and has broad applications in compute…
StdGEN: Semantic-Decomposed 3D Character Generation from Single Images
Yuze He, Yanning Zhou, Wang Zhao +5
We present StdGEN, an innovative pipeline for generating semantically decomposed high-quality 3D characters from single images, enabling broad applications in virtual reality, gami…
Boosting Consistency in Story Visualization with Rich-Contextual Conditional Diffusion Models
Fei Shen, Hu Ye, Sibo Liu +4
Recent research showcases the considerable potential of conditional diffusion models for generating consistent stories. However, current methods, which predominantly generate stori…
V-Express: Conditional Dropout for Progressive Training of Portrait Video Generation
Cong Wang, Kuan Tian, Jun Zhang +7
In the field of portrait video generation, the use of single images to generate portrait videos has become increasingly prevalent. A common approach involves leveraging generative…
Ensembling Diffusion Models via Adaptive Feature Aggregation
Cong Wang, Kuan Tian, Yonghang Guan +4
The success of the text-guided diffusion model has inspired the development and release of numerous powerful diffusion models within the open-source community. These models are typ…
PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models
Simin Chen, Xiaoning Feng, Xiaohong Han +2
In recent times, a plethora of Large Code Generation Models (LCGMs) have been proposed, showcasing significant potential in assisting developers with complex programming tasks. Ben…
Effortless Cross-Platform Video Codec: A Codebook-Based Method
Kuan Tian, Yonghang Guan, Jinxi Xiang +3
Under certain circumstances, advanced neural video codecs can surpass the most complex traditional codecs in their rate-distortion (RD) performance. One of the main reasons for the…
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach
Feng Luo, Jinxi Xiang, Jun Zhang +2
The recent use of diffusion prior, enhanced by pre-trained text-image models, has markedly elevated the performance of image super-resolution (SR). To alleviate the huge computatio…
Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models
Fei Shen, Hu Ye, Jun Zhang +3
Recent work has showcased the significant potential of diffusion models in pose-guided person image synthesis. However, owing to the inconsistency in pose between the source and ta…
Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
Kuan Tian, Yonghang Guan, Jinxi Xiang +3
The state-of-the-art neural video codecs have outperformed the most sophisticated traditional codecs in terms of RD performance in certain cases. However, utilizing them for practi…