Pose-Aware Diffusion (PAD): Direct 3D Generation in Target Pose

> Paper: Pose-Aware Diffusion for 3D Generation > Authors: Zihan Zhou, Luxi Chen, Jingzhi Zhou, Yuhao Wan, Min Zhao, Baoyu Fan, Chongxuan Li > arXiv: 2605.00345 | 2026-04-29

The Problem: Manually Posing Generated 3D Objects

Imagine designing a game character. The traditional 3D generation workflow looks like this:

1. Generate a 3D model in canonical (standard) pose 2. Manually rotate it to the target pose 3. Problems:

• Rotation can cause deformation
• Spatial mismatch
• Pose ambiguity
• Requires post-processing

It is like generating a "standing figure" and then forcibly bending it into a "running pose"—joints may twist, clothing may clip, and the result looks unnatural.

What is actually needed:

• Direct generation in the target pose
• No intermediate steps
• End-to-end
• Spatially consistent

PAD: Generating Directly in Observation Space

The paper proposes Pose-Aware Diffusion (PAD):

> Abandon the "canonicalize-then-rotate" paradigm. Generate 3D geometry directly in observation space, using monocular depth as a 3D geometric anchor to resolve pose ambiguity at its root.

Technical approach:

1. Observation-space generation — no canonical pose; generate directly at the target pose, one step, no ambiguity. 2. Monocular depth back-projection — 2D depth maps are back-projected into partial point clouds, serving as 3D geometric anchors that provide spatial constraints. 3. Pose injection — pose information is explicitly injected as a condition, not learned implicitly, giving precise control. 4. End-to-end diffusion — from noise to 3D, with the target pose as a conditioning signal; no post-processing needed.

An analogy:

• Traditional methods = sculpt a clay figure, then bend it into position
• PAD = sculpt the clay directly into the target pose

Why Direct Generation Beats "Canonicalize + Rotate"

Problems with traditional methods:

• Spatial mismatch: canonical and target poses have different geometry; rotation introduces deformation
• Pose ambiguity: correspondences between poses of the same object are uncertain, making learning hard
• Complex post-processing: manual adjustment, low automation, poor efficiency

PAD's advantages:

• Spatial consistency: generation happens in the target space directly; no rotation-induced deformation
• No ambiguity: pose is an explicit conditioning input, making the generation target clear and learning simpler
• End-to-end: single-step generation, no post-processing, higher efficiency

A Feynman-Style Judgment: Doing the Right Thing Matters More Than Doing Things Right

> "Generating in canonical pose and then rotating is 'correctly doing the wrong thing.' The root problem is that an object's pose cannot be fixed by post-processing—pose is part of the generation itself. PAD's insight is to generate directly in the target pose space, eliminating ambiguity at its source."

This reflects a design principle:

• Solve the root cause > patch symptoms
• The right problem > an elegant solution
• End-to-end > pipeline

Takeaways

If you work on generative models or 3D vision, ask yourself:

1. Does my generation pipeline contain unnecessary intermediate steps? 2. Is pose part of generation rather than post-processing? 3. Can depth information serve as a 3D anchor? 4. Is end-to-end generation better than a pipeline?

PAD reminds us: the best 3D generation is not "generate + transform" but "generate directly."

When diffusion models learn to directly sculpt 3D objects in the target pose within observation space, they evolve from "canonical model generators" into "pose-aware sculptors." In 3D generation, pose is not clothing—it is the skeleton.

*(Note: arXiv ID 2605.00345 and date 2026-04-29 are cited as given in the original post; verify against the official listing.)*