92 citations · 262 across the 21 of their papers we have counts for
21 papers
RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models
Bowen Zhang, Yiji Cheng, Chunyu Wang +6
We present RodinHD, which can generate high-fidelity 3D avatars from a portrait image. Existing methods fail to capture intricate details such as hairstyles which we tackle in this…
High-Quality 3D Creation from A Single Image Using Subject-Specific Knowledge Prior
Nan Huang, Ting Zhang, Yuhui Yuan +2
In this paper, we address the critical bottleneck in robotics caused by the scarcity of diverse 3D data by presenting a novel two-stage approach for generating high-quality 3D mode…
VolumeDiffusion: Flexible Text-to-3D Generation with Efficient Volumetric Encoder
Zhicong Tang, Shuyang Gu, Chunyu Wang +4
This paper introduces a pioneering 3D volumetric encoder designed for text-to-3D generation. To scale up the training data for the diffusion model, a lightweight network is develop…
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
Zigang Geng, Binxin Yang, Tiankai Hang +8
We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions. Unlike existing approaches that integrate prior knowledge…
Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior
Junshu Tang, Tengfei Wang, Bo Zhang +4
In this work, we investigate the problem of creating high-fidelity 3D content from only a single image. This is inherently challenging: it essentially involves estimating the under…
IRGen: Generative Modeling for Image Retrieval
Yidan Zhang, Ting Zhang, Dong Chen +11
While generative modeling has become prevalent across numerous research fields, its integration into the realm of image retrieval remains largely unexplored and underjustified. In…
CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet
Xiaoyi Dong, Jianmin Bao, Ting Zhang +7
Recent studies have shown that CLIP has achieved remarkable success in performing zero-shot inference while its fine-tuning performance is not satisfactory. In this paper, we ident…
Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion
Tengfei Wang, Bo Zhang, Ting Zhang +8
This paper presents a 3D generative model that uses diffusion models to automatically generate 3D digital avatars represented as neural radiance fields. A significant challenge in…
Paint by Example: Exemplar-based Image Editing with Diffusion Models
Binxin Yang, Shuyang Gu, Bo Zhang +5
Language-guided image editing has achieved great success recently. In this paper, for the first time, we investigate exemplar-guided image editing for more precise control. We achi…
3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation
Junshu Tang, Bo Zhang, Binxin Yang +4
In contrast to the traditional avatar creation pipeline which is a costly process, contemporary generative approaches directly learn the data distribution from photographs. While p…
MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining
Xiaoyi Dong, Jianmin Bao, Yinglin Zheng +9
This paper presents a simple yet effective framework MaskCLIP, which incorporates a newly proposed masked self-distillation into contrastive language-image pretraining. The core id…
Bootstrapped Masked Autoencoders for Vision BERT Pretraining
Xiaoyi Dong, Jianmin Bao, Ting Zhang +6
We propose bootstrapped masked autoencoders (BootMAE), a new approach for vision BERT pretraining. BootMAE improves the original masked autoencoders (MAE) with two core designs: 1)…
Pretraining is All You Need for Image-to-Image Translation
Tengfei Wang, Ting Zhang, Bo Zhang +4
We propose to use pretraining to boost general image-to-image translation. Prior image-to-image translation methods usually need dedicated architectural design and train individual…
Protecting Celebrities from DeepFake with Identity Consistency Transformer
Xiaoyi Dong, Jianmin Bao, Dongdong Chen +6
In this work we propose Identity Consistency Transformer, a novel face forgery detection method that focuses on high-level semantics, specifically identity information, and detecti…
Semi-Supervised Image-to-Image Translation using Latent Space Mapping
Pan Zhang, Jianmin Bao, Ting Zhang +2
Recent image-to-image translation works have been transferred from supervised to unsupervised settings due to the expensive cost of capturing or labeling large amounts of paired da…
General Facial Representation Learning in a Visual-Linguistic Manner
Yinglin Zheng, Hao Yang, Ting Zhang +7
How to learn a universal facial representation that boosts all face analysis tasks? This paper takes one step toward this goal. In this paper, we study the transfer performance of…
PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers
Xiaoyi Dong, Jianmin Bao, Ting Zhang +7
This paper explores a better prediction target for BERT pre-training of vision transformers. We observe that current prediction targets disagree with human perception judgment.This…
Robust Mutual Learning for Semi-supervised Semantic Segmentation
Pan Zhang, Bo Zhang, Ting Zhang +2
Recent semi-supervised learning (SSL) methods are commonly based on pseudo labeling. Since the SSL performance is greatly influenced by the quality of pseudo labels, mutual learnin…
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
Pan Zhang, Bo Zhang, Ting Zhang +3
Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels…
CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation
Xingran Zhou, Bo Zhang, Ting Zhang +5
We present the full-resolution correspondence learning for cross-domain images, which aids image translation. We adopt a hierarchical strategy that uses the correspondence from coa…
Face X-ray for More General Face Forgery Detection
Lingzhi Li, Jianmin Bao, Ting Zhang +4
In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveal…