A Simple Strategy for Body Estimation from Partial-View Images
arxiv(2024)
摘要
Virtual try-on and product personalization have become increasingly important
in modern online shopping, highlighting the need for accurate body measurement
estimation. Although previous research has advanced in estimating 3D body
shapes from RGB images, the task is inherently ambiguous as the observed scale
of human subjects in the images depends on two unknown factors: capture
distance and body dimensions. This ambiguity is particularly pronounced in
partial-view scenarios. To address this challenge, we propose a modular and
simple height normalization solution. This solution relocates the subject
skeleton to the desired position, thereby normalizing the scale and
disentangling the relationship between the two variables. Our experimental
results demonstrate that integrating this technique into state-of-the-art human
mesh reconstruction models significantly enhances partial body measurement
estimation. Additionally, we illustrate the applicability of this approach to
multi-view settings, showcasing its versatility.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要