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Found 20 papers, 14 papers with code
InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization
Recent strides in the development of diffusion models, exemplified by advancements such as Stable Diffusion, have underscored their remarkable prowess in generating visually compelling images.
DivAvatar: Diverse 3D Avatar Generation with a Single Prompt
We design DivAvatar, a novel framework that generates diverse avatars, empowering 3D creatives with a multitude of distinct and richly varied 3D avatars from a single text prompt.
En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D Synthetic Data
We present En3D, an enhanced generative scheme for sculpting high-quality 3D human avatars.
DiffusionGAN3D: Boosting Text-guided 3D Generation and Domain Adaptation by Combining 3D GANs and Diffusion Priors
Extensive experiments demonstrate that the proposed framework achieves excellent results in both domain adaptation and text-to-avatar tasks, outperforming existing methods in terms of generation quality and efficiency.
DreaMoving: A Human Video Generation Framework based on Diffusion Models
In this paper, we present DreaMoving, a diffusion-based controllable video generation framework to produce high-quality customized human videos.
Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion Models
This is a technical report on the 360-degree panoramic image generation task based on diffusion models.
Boosting3D: High-Fidelity Image-to-3D by Boosting 2D Diffusion Prior to 3D Prior with Progressive Learning
After the progressive training, the LoRA learns the 3D information of the generated object and eventually turns to an object-level 3D prior.
A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images
Meanwhile, 3D priors of facial details are incorporated to enhance the accuracy and authenticity of the reconstruction results.
Ranked #3 on
3D Face Reconstruction
on REALY (side-view)
Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution
In contrast to fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of simple box annotations, which has recently attracted increasing research attention.
Box-supervised Instance Segmentation with Level Set Evolution
A simple mask supervised SOLOv2 model is adapted to predict the instance-aware mask map as the level set for each instance.
Towards Counterfactual Image Manipulation via CLIP
In addition, we design a simple yet effective scheme that explicitly maps CLIP embeddings (of target text) to the latent space and fuses them with latent codes for effective latent code optimization and accurate editing.
DCT-Net: Domain-Calibrated Translation for Portrait Stylization
This paper introduces DCT-Net, a novel image translation architecture for few-shot portrait stylization.
Improving Nighttime Driving-Scene Segmentation via Dual Image-adaptive Learnable Filters
With DIAL-Filters, we design both unsupervised and supervised frameworks for nighttime driving-scene segmentation, which can be trained in an end-to-end manner.
Structure-Aware Flow Generation for Human Body Reshaping
Body reshaping is an important procedure in portrait photo retouching.
Unpaired Cartoon Image Synthesis via Gated Cycle Mapping
Experimental results demonstrate the superiority of the proposed method over the state of the art and validate its effectiveness in the brand-new task of general cartoon image synthesis.
ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo
The network is mainly composed of two components: a context-aware local retouching layer (LRL) and an adaptive blend pyramid layer (BPL).
Attention-guided Temporally Coherent Video Object Matting
Experimental results show that our method can generate high-quality alpha mattes for various videos featuring appearance change, occlusion, and fast motion.
PPR10K: A Large-Scale Portrait Photo Retouching Dataset with Human-Region Mask and Group-Level Consistency
HRP requires that more attention should be paid to human regions, while GLC requires that a group of portrait photos should be retouched to a consistent tone.
Active Boundary Loss for Semantic Segmentation
This paper proposes a novel active boundary loss for semantic segmentation.
Boosting Semantic Human Matting with Coarse Annotations
In this paper, we propose to use coarse annotated data coupled with fine annotated data to boost end-to-end semantic human matting without trimaps as extra input.
Ranked #9 on
Image Matting
on AM-2K
