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arxiv: 2607.01222 · v1 · pith:RSDDWORFnew · submitted 2026-07-01 · 💻 cs.CV

Ink3D: Sculpting 3D Assets with Extremely Complex Textures via Video Generative Models

Yue Han , Chong Li , Zhening Liu , Cong Huang , Fang Deng , Yong Liu , Fangyun Wei , Yan Lu This is my paper

Pith reviewed 2026-07-02 13:14 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D texture synthesisvideo generative modelsmulti-view integrationneural bakingcomplex surface textures3D asset generation
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0 comments X

The pith

Ink3D decouples geometry from texture to use video models for complex 3D appearances.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

3D generative models often produce high-quality shapes but struggle with intricate textures because large 3D datasets with rich details are rare. Video generative models, trained on far larger datasets, handle complex visual patterns effectively. The Ink3D framework first creates a basic white geometry mesh, then uses a conditional video model called OrbitPainter to generate videos showing the object from many orbiting viewpoints, and finally applies a neural module called TextureOptimizer to combine those views into consistent textures while correcting for any mismatches. This approach aims to produce textures that are richer and more accurate to given references than methods relying only on 3D data.

Core claim

By first reconstructing a white-mesh geometry with an off-the-shelf 3D model, then generating dense orbit-scan videos with OrbitPainter to capture appearance across viewpoints, and integrating them with TextureOptimizer to handle inconsistencies, Ink3D bridges 3D generation with large-scale video priors for complex texture synthesis.

What carries the argument

OrbitPainter for producing multi-view video observations and TextureOptimizer for baking them into coherent textures on a decoupled geometry.

If this is right

  • Produces significantly richer textures than prior 3D generative approaches.
  • Enables more faithful texture generation from reference images.
  • Leverages abundant video training data to overcome scarcity of detailed 3D data.
  • Allows high-quality 3D assets with complex surface patterns like fine engravings or fabrics.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Future improvements in video models could directly enhance 3D texture quality without changes to the geometry pipeline.
  • Similar decoupling might apply to other modalities where one data type is abundant and another is scarce.
  • The method could support interactive editing if the video generation allows conditioning on user inputs.

Load-bearing premise

The multi-view videos from the conditional video model are consistent enough to be integrated into a single texture despite geometry inconsistencies introduced by the generation process.

What would settle it

Running the system on reference images with very fine, non-periodic details and checking if the output textures show blurring, seams, or loss of detail compared to the input.

Figures

Figures reproduced from arXiv: 2607.01222 by Chong Li, Cong Huang, Fang Deng, Fangyun Wei, Yan Lu, Yong Liu, Yue Han, Zhening Liu.

Figure 1
Figure 1. Figure 1: Ink3D introduces geometry-aware video generation into the 3D generation pipeline to synthesize 3D assets with extremely complex textures such as intricate clothing patterns and high-frequency decorative details. Abstract. Recent 3D generative models can synthesize high-quality ge￾ometry but often struggle to reproduce intricate textures from reference images, largely due to the scarcity of large-scale 3D t… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of OrbitPainter. (a) Given a 3D asset, we first apply random light￾ing and render it from a randomly sampled viewpoint to obtain a reference image. We then perform horizontal and vertical orbit scans to render RGB videos, surface￾normal videos, and 3D-position videos. All videos, together with the reference image, are encoded by a video VAE encoder into latent representations. (b) OrbitPainter is … view at source ↗
Figure 3
Figure 3. Figure 3: (a) Inference pipeline of Ink3D. Given a reference image, we first esti￾mate the corresponding white-mesh geometry using an off-the-shelf geometry genera￾tion model. The trained OrbitPainter then generates orbit-scan videos of the object, whose frames are collected to form a dense-view image set. Finally, TextureOptimizer bakes these dense views onto the white-mesh geometry to produce the final textured 3D… view at source ↗
read the original abstract

Recent 3D generative models can synthesize high-quality geometry but often struggle to reproduce intricate textures from reference images, largely due to the scarcity of large-scale 3D training data with rich surface appearance. In contrast, visual generative models are trained on datasets several orders of magnitude larger and excel at modeling complex visual patterns. Motivated by this gap, we introduce Ink3D, a framework that bridges 3D generation with large-scale video generative models to synthesize extremely complex textures. Ink3D first reconstructs a white-mesh geometry using an off-the-shelf 3D generation model. It then employs OrbitPainter, a conditional video generative model, to produce dense orbit-scan videos capturing object appearance across viewpoints. To convert these views into coherent textures, we introduce TextureOptimizer, a neural baking module that integrates dense multi-view observations while mitigating geometry inconsistencies arising from video generation. By decoupling geometry and texture synthesis and leveraging large-scale pretrained video priors, Ink3D enables significantly richer and more faithful texture generation than prior approaches.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript proposes Ink3D, a framework for generating 3D assets with extremely complex textures. It reconstructs a white-mesh geometry using an off-the-shelf 3D model, employs OrbitPainter (a conditional video generative model) to synthesize dense orbit-scan videos capturing appearance across viewpoints, and uses TextureOptimizer (a neural baking module) to integrate the multi-view observations into coherent textures while mitigating geometry inconsistencies from the video generation process. The central claim is that decoupling geometry and texture synthesis and leveraging large-scale pretrained video priors enables significantly richer and more faithful texture generation than prior 3D approaches.

Significance. If the result holds, the work would be significant because it directly addresses the data scarcity issue in 3D texture modeling by transferring priors from much larger video datasets. The decoupling strategy and use of orbit videos represent a practical bridge between modalities, and the approach could enable new applications in asset creation where intricate surface details are required.

major comments (1)
  1. [§3 (TextureOptimizer)] §3 (TextureOptimizer): The claim that TextureOptimizer 'mitigates geometry inconsistencies arising from video generation' is load-bearing for the central claim of faithful textures, yet the manuscript provides no derivation, objective-function analysis, or bound demonstrating that the neural baking remains well-posed when viewpoint-dependent shape deviations exceed the optimizer's regularization; without this, residual view conflicts could produce seams or blurring rather than faithful appearance.
minor comments (2)
  1. [Abstract and §3] The abstract and method overview would benefit from a high-level equation or pseudocode sketch of the TextureOptimizer objective to clarify how multi-view fusion is formulated.
  2. [Experiments] Quantitative metrics (e.g., perceptual texture fidelity scores, user studies) and ablations on geometry-drift magnitude are referenced only at a high level; explicit tables comparing against baselines would strengthen the 'significantly richer' claim.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the role of TextureOptimizer. We respond to the single major comment below.

read point-by-point responses

  1. Referee: [§3 (TextureOptimizer)] §3 (TextureOptimizer): The claim that TextureOptimizer 'mitigates geometry inconsistencies arising from video generation' is load-bearing for the central claim of faithful textures, yet the manuscript provides no derivation, objective-function analysis, or bound demonstrating that the neural baking remains well-posed when viewpoint-dependent shape deviations exceed the optimizer's regularization; without this, residual view conflicts could produce seams or blurring rather than faithful appearance.

    Authors: We acknowledge that the manuscript does not contain a formal derivation, objective-function analysis, or theoretical bound establishing well-posedness of the neural baking under large viewpoint-dependent geometry deviations. The TextureOptimizer is presented as a practical neural-field optimization that combines dense photometric and perceptual losses with view-consistency regularizers; the design relies on the high density of orbit views to average out inconsistencies rather than on a proven tolerance bound. Because the central claim of faithful texture transfer rests on this mitigation step, the absence of such analysis is a substantive gap. We will add, in the revised manuscript, an explicit statement of the composite objective, a short discussion of its conditioning under geometry perturbations, and supporting ablation results on controlled synthetic deviations. revision: yes

Circularity Check

0 steps flagged

No circularity: framework relies on external pretrained models without self-referential reductions

full rationale

The provided abstract and description contain no equations, fitted parameters, or derivation steps that reduce claims to inputs by construction. Ink3D is described as using off-the-shelf 3D models and large-scale pretrained video generative models (external to the paper), with TextureOptimizer introduced as a new module to integrate observations. No self-citations, ansatzes smuggled via prior work, or renaming of known results appear in the load-bearing claims. The central premise of decoupling geometry and texture synthesis is presented as building on independent external priors rather than being defined in terms of its own outputs. This is the common case of a self-contained engineering framework without circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no explicit free parameters, axioms, or invented physical entities are stated. The two named modules (OrbitPainter, TextureOptimizer) are engineering constructs rather than new physical postulates.

pith-pipeline@v0.9.1-grok · 5727 in / 1043 out tokens · 17860 ms · 2026-07-02T13:14:17.946348+00:00 · methodology

discussion (0)

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