Search Results for author:
Found 9 papers, 6 papers with code
StableGarment: Garment-Centric Generation via Stable Diffusion
In this paper, we introduce StableGarment, a unified framework to tackle garment-centric(GC) generation tasks, including GC text-to-image, controllable GC text-to-image, stylized GC text-to-image, and robust virtual try-on.
Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model
Current makeup transfer methods are limited to simple makeup styles, making them difficult to apply in real-world scenarios.
InstantID: Zero-shot Identity-Preserving Generation in Seconds
There has been significant progress in personalized image synthesis with methods such as Textual Inversion, DreamBooth, and LoRA.
Ranked #2 on
Diffusion Personalization Tuning Free
on AgeDB
SSR-Encoder: Encoding Selective Subject Representation for Subject-Driven Generation
Recent advancements in subject-driven image generation have led to zero-shot generation, yet precise selection and focus on crucial subject representations remain challenging.
StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences
To address this issue, multi-frame optical flow methods leverage adjacent frames to mitigate the local ambiguity.
IPDreamer: Appearance-Controllable 3D Object Generation with Image Prompts
Recent advances in 3D generation have been remarkable, with methods such as DreamFusion leveraging large-scale text-to-image diffusion-based models to supervise 3D generation.
STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection
To solve this problem, we propose a Self-adapTive Ambiguity Reduction (STAR) loss by exploiting the properties of semantic ambiguity.
Ranked #1 on
Face Alignment
on 300W
An Empirical Study of Propagation-based Methods for Video Object Segmentation
While propagation-based approaches have achieved state-of-the-art performance for video object segmentation, the literature lacks a fair comparison of different methods using the same settings.
Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis
Mutually leveraging both low-level feature sharing and high-level prediction correlating, our MTRCNet-CL method can encourage the interactions between the two tasks to a large extent, and hence can bring about benefits to each other.
Ranked #3 on
Surgical tool detection
on Cholec80
