[SDFit: 3D Object Pose and Shape by Fitting a Morphable SDF to a Single Image]: We focus on recovering 3D object pose and shape from single images. This is highly challenging due to strong (self-)occlusions， depth ambiguities， the enormous shape variance， and lack of 3D ground truth for natural images. Recent work relies mostly on learning from finite datasetsセフレ 巨乳， so it struggles generalizingセフレ 巨乳， while it focuses mostly on the shape itself， largely ignoring the alignment with pixels. Moreover， it performs feed-forward inference， so it cannot refine estimates. We tackle these limitations with a novel framework， called SDFit. To this end， we make three key observations: (1) Learned signed-distance-function (SDF) models act as a strong morphable shape prior. (2) Foundational models embed 2D images and 3D shapes in a joint space， and (3) also infer rich features from images. SDFit exploits these as follows. First， it uses a ......chloe 调教