SketchImage → Imageacademic

UltraShape 1.0 VAE Pipeline Overview

Figure 2 : Overview of UltraShape 1.0 pipeline , where Enc. and Dec. represent the encoder and decoder of our VAE. The superscript “2” on the upper right corner denotes the Stage-2 model. MC means marching cube.

Input image
Generated result

Paper context

Paper title: UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement Abstract: In this report, we introduce UltraShape 1.0, a scalable 3D diffusion framework for high-fidelity 3D geometry generation. The proposed approach adopts a two-stage generation pipeline: a coarse global structure is first synthesized and then refined to produce detailed, high-quality geometry. To support reliable 3D generation, we develop a comprehensive data processing pipeline that includes a novel watertight processing method and high-quality data filtering. This pipeline improves the geometric quality of publicly available 3D datasets by removing low-quality samples, filling holes, and thickening thin structures, while preserving fine-grained geometric details. To enable fine-grained geometry refinement, we decouple spatial localization from geometric detail synthesis in the diffusion process. We achieve this by performing voxel-based refinement at fixed spatial locations, where voxel queries derived from coarse geometry provide explicit positional anchors encoded via RoPE, allowing the diffusion model to focus on synthesizing local geometric details within a reduced, structured solution space. Our model is trained exclusively on publicly available 3D datasets, achieving strong geometric quality despite limited training resources. Extensive evaluations demonstrate that UltraShape 1.0 performs competitively with existing open-source methods in both data processing quality and geometry generation. All code and trained models will be released to support future research. Passages referencing this figure: 0/ \titlefigure Figure 1 : High-quality 3D assets generated by UltraShape 1.

The prompt

A reference image is attached above. It is my rough sketch of what I
want my final figure to look like — sometimes hand-drawn, sometimes
an AI quick-draft. The quality is rough; details may be wrong; some
elements may be missing — but it shows the STRUCTURE / SPATIAL LAYOUT
I'm going for.

I've also shared the paper title + abstract + method section + figure
caption + paragraphs that reference this figure.

TASK: Refine my rough sketch into a polished publication-quality figure.

  - Preserve the SPATIAL STRUCTURE of the sketch: where the boxes are,
    how they connect, the overall reading order, the rough proportions.
  - You may correct details: better text labels (use the paper context
    to get the right component names), cleaner shapes, real icons
    instead of stick-figure placeholders.
  - Do NOT regenerate from scratch with a different layout. The
    finished figure must be visibly the same composition as the sketch.

If your output bears no spatial resemblance to the reference sketch,
you've failed the task. Refine the sketch — don't replace it. Just
give me the polished figure.

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Open it inside the generator with the prompt pre-filled.

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