Revisiting Avatar-As-Image: High-Fidelity Registration is All You Need

The representation of 3D clothed humans as standardized 2DUV texture and displacement maps over an underlying body model haslong been studied. This compact representation is enticing as it enablespretrained image networks to process, generate, and edit 3D avatars, butis only useful if scans are accurately aligned and brought into correspon-dence via high-fidelity registration. This prerequisite has never been met,which we argue explains the limited quality of prior UV-based methodsfor clothed humans. Despite its significance, no public method produceshigh-fidelity SMPL(−X)+D registrations with UV texture from arbitraryclothed scans. We present AvaImg, a multi-stage optimization pipeline, toclose this gap: it enforces body-inside-clothing constraint via signed wind-ing numbers, made viable by a three-level efficiency cascade (∼10× run-time reduced, ∼95% storage saved), and recovers fine surface detail usingcoarse-to-fine displacement optimization. AvaImg outperforms all base-lines in body fitting, shape estimation, and surface registration acrosssix datasets, yielding textured registrations near-indistinguishable fromscans (PSNR=34.48dB). For validation of AvaImg’s Avatar-as-Image rep-resentation as imminently compatible with image foundation models, weauto-encode our UV maps via the frozen FLUX VAE. This achieves only0.76mm added Chamfer error relative to scan and shows that the result-ing maps lie within natural-image distributions, supporting the use of 2Dgenerative priors for 3D avatar generation. Code, data, and Singularitycontainers will be publicly released.