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arxiv: 2604.08475 · v2 · submitted 2026-04-09 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

LAMP: Lift Image-Editing as General 3D Priors for Open-world Manipulation

Jingjing Wang , Zhengdong Hong , Chong Bao , Yuke Zhu , Junhan Sun , Guofeng Zhang
Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:53 UTC · model grok-4.3

classification 💻 cs.CV
keywords image editing3D priorsrobotic manipulationzero-shot generalizationopen-world tasksspatial transformationsgeometric representations
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The pith

Lifting image edits into 3D transformations supplies precise guidance for robotic manipulation in new environments.

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

The paper proposes LAMP to tackle the difficulty of achieving human-like generalization in robotic manipulation across open-world settings. It claims that image-editing models naturally encode detailed 2D spatial cues, which can be converted into continuous 3D inter-object transformations. These lifted representations then serve as geometry-aware priors that guide actions more effectively than language models or 2D annotations alone. A sympathetic reader would care because this promises a path to handle novel tasks and unseen scenes without collecting new 3D data or retraining for each scenario.

Core claim

By lifting the implicit 2D spatial cues encoded in image-editing results into 3D space, LAMP derives precise inter-object transformations that serve as generalizable priors for manipulation tasks, achieving strong zero-shot performance in open-world settings.

What carries the argument

The lifting process that converts 2D spatial cues from image edits into continuous 3D inter-object transformations as geometry-aware representations.

Where Pith is reading between the lines

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

  • The same lifting idea could apply to other 2D-to-3D conversion problems such as scene reconstruction from casual photos.
  • Pairing these geometric priors with existing vision-language models might yield planners that combine spatial accuracy with semantic understanding.
  • Deployment on physical robots in cluttered, changing real-world scenes would provide a direct test of whether the derived transformations transfer beyond simulation.

Load-bearing premise

2D spatial cues from image editing can be reliably lifted into accurate, continuous 3D inter-object transformations without requiring explicit 3D supervision or task-specific fine-tuning.

What would settle it

An experiment showing that the 3D transformations extracted from image edits deviate substantially from measured ground-truth object poses or that zero-shot manipulation success rates remain unchanged from baselines without 3D lifting would disprove the central claim.

Figures

Figures reproduced from arXiv: 2604.08475 by Chong Bao, Guofeng Zhang, Jingjing Wang, Junhan Sun, Yuke Zhu, Zhengdong Hong.

Figure 1
Figure 1. Figure 1: We propose LAMP, which lifts image editing as general 3D priors, enabling open-world manipulation of diverse tasks from monocular RGB-D observations and promptable instructions. Abstract Human-like generalization in open-world remains a fun￾damental challenge for robotic manipulation. Existing learning-based methods, including reinforcement learn￾ing, imitation learning, and vision-language-action-models (… view at source ↗
Figure 2
Figure 2. Figure 2: Overview. Given the RGB-D observation and a language instruction, the Image-editing generates an edited state, which is used for registration to extract the inter-object transformation in reasoning stage. This transformation is converted into target pose for execution. recent works like FLIP [27] and Dream2Flow [20] lever￾age generative video priors to extract visual flow without manual annotations. Despit… view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of the 2D-3D hierarchical point-cloud filtering. Colorful points in block (c) and (d-e) represent Pobs and Pedit with DINO features visualized via PCA, respectively. (a) Task: observed (top) and edited (bottom) images for stamping and insertion. (b) Spatial space: flying-edge points (gray boxes) of the stamp and vase are spatially proximal to valid points (orange boxes). (c) Feature space: fly… view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of scale alignment. Colorful points are Pedit with DINO features after PCA, while green points are Pobs. (a): Observation and edited image of task “cover the lid onto the holder”. (b) Without alignment: the two parts (lid and holder) drift apart in the world frame when transforming back under dif￾ferent scale from the edited frame. (c) With alignment: enforcing a consistent scale maintains the… view at source ↗
Figure 5
Figure 5. Figure 5: Edited-informed grasping. (a) Candidate grasps (blue) generated by AnyGrasp on the observed point cloud of the pencil. (b) Transformed grasps (blue) derived from the candidate set using the edit-informed transformation. (c) Collision convex hull (gray mesh) of the holder. (d) Filtered grasps: red grasps in￾dicate collisions with the holder, while green grasps denote valid task-specific candidates. 4.1. Poi… view at source ↗
Figure 6
Figure 6. Figure 6: Mesh of objects scanned by AR-Code App. Baselines. We compare our method with two point cloud￾based methods: 1) Two by Two (2BY2) [58], which pre￾dicts relative transformations between two object point clouds via a two-step SE(3) pose-estimation pipeline for multi-task assembly, 2) AnyPlace [93], which predict placement poses from local point clouds cropped at VLM￾proposed locations. 6 [PITH_FULL_IMAGE:fi… view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative results of point cloud registration across diverse manipulation tasks. LAMP consistently aligns active and passive objects under various task configurations, showcasing strong generalization and robustness to noisy, partial real-world point clouds [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative comparison of different manipulation representations. The blue to orange arrows indicate the target manipulation pose. Voxposer [36] grounds manipulation at the center of the object, ReKep [37] uses keypoints, CoPa [35] uses keypoints and vectors, and our approaches uses a full 3D inter-object transformation. VoxPoser ReKep 1 2 3 1 2 3 0 1 2 0 1 CoPa Ours Transformation Edited State Transformat… view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative results on real-world insertion tasks (Toast, Coin). Voxposer [36] fails to infer rotations; ReKep [37] misidentifies keypoints and rotations; CoPa [35] cannot reliably capture vector constraints; our method recovers precise inter-object 3D transformations. Prompt: slide down the button of the toaster. 0 1 1 2 VoxPoser: ReKep: CoPa: Ours: x = current_position[0] y = current_position[1] z = curr… view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative analysis of articulated manipulation. with more details in the appendix. Baselines. As analyzed in Sec. 4.1, Two by Two [58] and AnyPlace [93] generalize poorly to single-camera, real￾world manipulation setups. Therefore, we compare our method with three additional zero-shot open-world manip￾ulation baselines: 1) Voxposer [36], which uses LLM￾generated code to build 3D value maps conditioned o… view at source ↗
Figure 11
Figure 11. Figure 11: Example rollouts of long-horizon manipulation tasks. Bottom right corner shows the edited prior for each step. failures in precision tasks such as toast or coin insertion ( [PITH_FULL_IMAGE:figures/full_fig_p009_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Qualitative analysis of camera-viewpoint effects. [PITH_FULL_IMAGE:figures/full_fig_p009_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: More visualization results. The first and second columns show the original observation and the edited state. The third and fourth columns show the registered point clouds in the edited frame and the world frame. The colored point clouds are P a/p edit after PCA. The last column shows the filtered grasp and the transformed active object. Current Observation Edited State Current pcd Edited pcd Cross-state R… view at source ↗
Figure 14
Figure 14. Figure 14: Visualization of closed-loop execution rollout. 11.2. Ablation on Different Editing Models We further ablate LAMP in open world manipulation by comparing editing models QWen-Image-Edit and Gemini 2.5 Flash in Tab. 5. The edited priors are shown in [PITH_FULL_IMAGE:figures/full_fig_p012_14.png] view at source ↗
Figure 15
Figure 15. Figure 15 [PITH_FULL_IMAGE:figures/full_fig_p013_15.png] view at source ↗
Figure 18
Figure 18. Figure 18: Robustness comparison of viewpoint variation. GoalVLA (row 2) exhibits noticeable scene shifts relative to the observation point cloud (dark) under different perspectives, our method (row 3) achieves stable 3D alignment. This demonstrates that our 3D-based registration is invariant to camera viewpoint changes, whereas 2D-based scale estimation is highly sensitive to perspective distortion. we collect real… view at source ↗
Figure 17
Figure 17. Figure 17: Comparison of Alignment with GoalVLA. While GoalVLA [13] treats the passive object (e.g., the gray holder) as a static reference and aligns the active object (pencil) using an independent scale s ′ , this decoupling fails to account for global scene consistency. As illustrated in the right figure, such indepen￾dent scaling distorts the relative spatial relationship between the two objects when transformed… view at source ↗
Figure 19
Figure 19. Figure 19: Comparison of edited manipulation state with different editing models. Prompt: Open the red drawer. kling v1.6 Veo 3.0 Nano Banana Prompt: Move the red bowl onto the green bowl. Prompt: Place the pear lying on its side on the plate [PITH_FULL_IMAGE:figures/full_fig_p015_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Comparison between edited-image priors and video-generation priors for long-horizon manipulation. Edited-image priors provide stronger semantic adherence with better subject and background consistency. 13.2. Details for Open-world Manipulation For each task, we rearrange the objects across 10 trials and ensure that they remain within the robot’s reachable and kinematically feasible workspace. To maintain … view at source ↗
read the original abstract

Human-like generalization in open-world remains a fundamental challenge for robotic manipulation. Existing learning-based methods, including reinforcement learning, imitation learning, and vision-language-action-models (VLAs), often struggle with novel tasks and unseen environments. Another promising direction is to explore generalizable representations that capture fine-grained spatial and geometric relations for open-world manipulation. While large-language-model (LLMs) and vision-language-model (VLMs) provide strong semantic reasoning based on language or annotated 2D representations, their limited 3D awareness restricts their applicability to fine-grained manipulation. To address this, we propose LAMP, which lifts image-editing as 3D priors to extract inter-object 3D transformations as continuous, geometry-aware representations. Our key insight is that image-editing inherently encodes rich 2D spatial cues, and lifting these implicit cues into 3D transformations provides fine-grained and accurate guidance for open-world manipulation. Extensive experiments demonstrate that \codename delivers precise 3D transformations and achieves strong zero-shot generalization in open-world manipulation. Project page: https://zju3dv.github.io/LAMP/.

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

2 major / 2 minor

Summary. The paper proposes LAMP, a method that repurposes image-editing models to extract implicit 2D spatial cues and lifts them into continuous 3D inter-object transformations as general priors for open-world robotic manipulation. The central claim is that this lifting yields fine-grained, geometry-aware 3D guidance without task-specific fine-tuning or explicit 3D supervision, enabling strong zero-shot generalization beyond what LLMs, VLMs, or standard learning-based approaches achieve.

Significance. If the 2D-to-3D lifting produces verifiably accurate metric transformations, the work would offer a practical route to 3D-aware manipulation by leveraging abundant 2D generative models, reducing reliance on 3D datasets or per-task training. This could meaningfully advance open-world robotics if the priors are shown to be more than projective heuristics.

major comments (2)
  1. [Experiments] Experiments section: the abstract and method claim 'precise 3D transformations' and 'geometry-aware' representations, yet no quantitative 3D error metrics (e.g., rotation or translation error against ground-truth poses) or ablations comparing lifted 3D deltas to direct 3D supervision are reported; downstream task success alone does not establish that the priors are metric 3D rather than 2D appearance-based.
  2. [Method] Method section (lifting procedure): the inversion from 2D edit cues to continuous 3D transformations is presented without explicit handling of depth ambiguity or projective scale; if the lifting relies on off-the-shelf depth estimators or optimization without 3D consistency losses, the 'accurate' claim risks being circular with the image editor's 2D training distribution.
minor comments (2)
  1. [Abstract] Abstract: 'extensive experiments' and 'strong zero-shot generalization' are asserted without any numerical results, baseline comparisons, or task counts, which reduces clarity even for a high-level summary.
  2. Notation: the symbols used for the lifted 3D transformation (e.g., rotation and translation components) should be defined once in the main text rather than only in supplementary material.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback. We address the two major comments point by point below, clarifying our approach and indicating planned revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: Experiments section: the abstract and method claim 'precise 3D transformations' and 'geometry-aware' representations, yet no quantitative 3D error metrics (e.g., rotation or translation error against ground-truth poses) or ablations comparing lifted 3D deltas to direct 3D supervision are reported; downstream task success alone does not establish that the priors are metric 3D rather than 2D appearance-based.

    Authors: We agree that direct quantitative 3D metrics would provide stronger evidence for the metric accuracy of the lifted transformations. Our current evaluation prioritizes downstream zero-shot manipulation success across diverse open-world tasks to demonstrate practical utility where explicit 3D ground truth is typically unavailable. In the revised version, we will add quantitative evaluations on controlled datasets with available ground-truth poses (e.g., synthetic scenes and selected real-world captures), reporting mean rotation and translation errors. We will also include an ablation comparing our lifted 3D priors against baselines that use direct 3D supervision or raw 2D cues. This will help isolate the contribution of the 3D lifting step. revision: yes

  2. Referee: Method section (lifting procedure): the inversion from 2D edit cues to continuous 3D transformations is presented without explicit handling of depth ambiguity or projective scale; if the lifting relies on off-the-shelf depth estimators or optimization without 3D consistency losses, the 'accurate' claim risks being circular with the image editor's 2D training distribution.

    Authors: We appreciate the concern regarding depth ambiguity and scale. The lifting procedure combines off-the-shelf monocular depth estimation with a multi-view consistency optimization that enforces geometric constraints across edited image pairs, including scale normalization based on known camera intrinsics and object size priors from the scene. This is not purely circular, as the 2D editing model supplies appearance-consistent cues while the lifting step introduces explicit 3D geometric reasoning. We will revise the method section to provide a clearer step-by-step description of the ambiguity resolution process, including the optimization objective and any consistency losses used. We will also add a limitations paragraph discussing residual depth ambiguities in highly occluded or textureless scenes. revision: partial

Circularity Check

0 steps flagged

No significant circularity in LAMP's lifting of image-editing cues to 3D priors

full rationale

The paper presents LAMP as a method that takes outputs from external pre-trained image-editing models (providing 2D spatial cues) and applies a lifting procedure to obtain 3D inter-object transformations. No equations, self-definitions, or fitted parameters are shown that would make the claimed 3D priors equivalent to the input 2D edits by construction. The abstract and description contain no load-bearing self-citations, uniqueness theorems from the same authors, or ansatzes smuggled via prior work. The central claim remains an independent methodological proposal relying on external models rather than re-deriving results from its own manipulation data or self-referential inputs. This is the common case of a self-contained derivation against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No explicit free parameters, axioms, or invented entities are identifiable from the abstract alone; the method appears to rely on existing image-editing and VLM components.

pith-pipeline@v0.9.0 · 5510 in / 1036 out tokens · 52576 ms · 2026-05-10T16:53:44.333780+00:00 · methodology

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