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DualPM: Dual Posed-Canonical Point Maps for 3D Shape and Pose Reconstruction

CVPR 2025 · 3D vision › Human pose estimation · approach: Learned camera pose estimation · 2 citations (OpenAlex)

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Introduces Dual Point Maps (DualPMs), a pair of canonical and posed point maps with a layered amodal extension, trained purely on synthetic renders of one or two quadruped models to reconstruct 3D shape and pose from a single image.

Problem

Monocular 3D shape and pose reconstruction of deformable quadruped objects, where a single point map gives geometry but not the deformation relative to a canonical rest pose, and visible-only maps miss self-occluded parts.

Approach

Predict two pixel-aligned point maps from one image: a posed camera-space point map and a canonical-space point map (viewpoint- and pose-invariant), so the deformation field is their difference; extend to layered amodal point maps via depth-peeling-style layers, and condition posed prediction on the canonical map.

Key results

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use DualPM reconstruction instead of 3D-FaunaQuadruped reconstruction · PASCAL VOC HorsePCK@0.1 (%): 53.9 -> 73.2Helped
Use DualPM reconstruction instead of MagicPonyQuadruped reconstruction · PASCAL VOC CowPCK@0.1 (%): 42.5 -> 66.86Helped
Use DualPM reconstruction instead of MagicPonyQuadruped reconstruction · PASCAL VOC SheepPCK@0.1 (%): 41.2 -> 66.82Helped
Use DualPM reconstruction instead of TrellisQuadruped reconstruction · Animodel HorseChamfer Distance (cm), real-sized: 2.23 ± 1.39 -> 1.52 ± 0.88Helped
Use DualPM reconstruction instead of TrellisQuadruped reconstruction · Animodel CowChamfer Distance (cm), real-sized: 1.85 ± 1.31 -> 1.11 ± 0.73Helped
Use DualPM reconstruction instead of TrellisQuadruped reconstruction · Animodel SheepChamfer Distance (cm), real-sized: 1.96 ± 1.22 -> 1.33 ± 0.78Helped
Predict point maps instead of bone rotations (ablation)DualPM on Animodel Horses · Animodel HorsesChamfer Distance (cm), real-sized: 4.96 ± 1.66 -> 3.13 ± 2.13Helped
Predict point maps instead of bone rotations (ablation)DualPM on Animodel Horses · Animodel HorsesChamfer Distance (cm), normalized: 1.78 ± 0.59 -> 1.11 ± 0.73Helped
Use 8 layers instead of 4 (ablation)DualPM on Animodel Horses · Animodel HorsesChamfer Distance (cm), real-sized: 3.13 ± 2.13 -> 3.13 ± 1.98No clear effect
Use 8 layers instead of 4 (ablation)DualPM on Animodel Horses · Animodel HorsesChamfer Distance (cm), normalized: 1.11 ± 0.73 -> 1.11 ± 0.69No clear effect
Condition P on image features F and canonical point maps Q rather than Q alone (ablation)DualPM on Animodel Horses · Animodel HorsesChamfer Distance (cm), real-sized: 3.13 ± 2.13 -> 3.29 ± 2.26Hurt
Condition P on image features F and canonical point maps Q rather than Q alone (ablation)DualPM on Animodel Horses · Animodel HorsesChamfer Distance (cm), normalized: 1.11 ± 0.73 -> 1.17 ± 0.80Hurt

Surprising findings

Limitations

Experimental setup

Datasets: Animodel PASCAL VOC Goat images from prior work

Benchmarks: PASCAL VOC keypoint transfer (PCK@0.1) Animodel bi-directional Chamfer distance

Metrics: PCK@0.1 bi-directional Chamfer distance (cm)

Baselines: A-CSM MagicPony Farm3D 3D-Fauna Trellis

Builds on: DUSt3R NOCS DINOv2 Farm3D 3D-Fauna

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