Search Results for author:
Found 7 papers, 2 papers with code
Exploring Disentangled and Controllable Human Image Synthesis: From End-to-End to Stage-by-Stage
Through experiments, we observe that simply incorporating the VTON dataset as additional data to train the end-to-end model degrades performance, primarily due to the inconsistency in data forms between the two datasets, which disrupts the disentanglement process.
1-2-1: Renaissance of Single-Network Paradigm for Virtual Try-On
Virtual Try-On (VTON) has become a crucial tool in ecommerce, enabling the realistic simulation of garments on individuals while preserving their original appearance and pose.
PICTURE: PhotorealistIC virtual Try-on from UnconstRained dEsigns
Unlike prior arts constrained by specific input types, our method allows flexible specification of style (text or image) and texture (full garment, cropped sections, or texture patches) conditions.
MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human Captures
In this era, the success of large language models and text-to-image models can be attributed to the driving force of large-scale datasets.
FashionTex: Controllable Virtual Try-on with Text and Texture
Virtual try-on attracts increasing research attention as a promising way for enhancing the user experience for online cloth shopping.
MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction
The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to predict the next frame in a recursive manner.
From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting
Despite video forecasting has been a widely explored topic in recent years, the mainstream of the existing work still limits their models with a single prediction space but completely neglects the way to leverage their model with multi-prediction spaces.
