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arxiv: 2605.22126 · v1 · pith:5CDQGDOKnew · submitted 2026-05-21 · 💻 cs.CV

AesFormer: Transform Everyday Photos into Beautiful Memories

Tianxiang Du , Hulingxiao He , Yuxin Peng This is my paper

Pith reviewed 2026-05-22 07:04 UTC · model grok-4.3

classification 💻 cs.CV
keywords Aesthetic Photo ReconstructionImage EditingAesFormerPhoto EnhancementStructural EditingAesthetic AssessmentBenchmark DatasetTwo-Stage Framework
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The pith

A two-stage model first plans aesthetic edits across seven photo dimensions then applies structural fixes to improve everyday snapshots.

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

Everyday photos frequently suffer from structural problems like poor composition or awkward poses that current retouching tools leave unaddressed. The paper defines Aesthetic Photo Reconstruction as the task of fixing these issues through targeted structural changes while keeping the people and scene recognizable. AesFormer tackles the problem by splitting the work into two stages: one stage analyzes the input along seven photographic dimensions and produces specific editing instructions, and the second stage executes those instructions on the image. This separation lets the system incorporate aesthetic judgment that direct editing models lack. The authors support the method with a new collection of aligned poor-to-good photo pairs mined from videos.

Core claim

AesFormer is a two-stage framework that decouples aesthetic planning from image editing. In the first stage an aesthetic action model analyzes the input photo along seven progressive photographic dimensions and outputs executable editing actions, with reinforcement learning applied to explore varied action plans. In the second stage an action-conditioned editor carries out the structural edits. The approach improves aesthetic quality on the APR task while preserving subject identity and scene semantics, as shown on a benchmark of 9,071 aligned image pairs.

What carries the argument

AesFormer, a two-stage framework that decouples an aesthetic action model analyzing inputs along seven photographic dimensions and generating editing actions from an action-conditioned editor that performs the structural edits.

If this is right

  • Structural flaws such as composition or pose problems become addressable through sequenced editing actions rather than end-to-end image generation.
  • Adding an upstream planning stage with reinforcement learning exploration improves the aesthetic outcomes of existing image editing models.
  • Video-based mining can supply large numbers of aligned poor-to-good image pairs suitable for training aesthetic reconstruction systems.
  • The separation of planning and execution makes it possible to evaluate and refine aesthetic decisions independently of pixel-level edits.

Where Pith is reading between the lines

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

  • The same planning-then-execution split could transfer to related tasks such as video stabilization or product photography enhancement.
  • Testing whether the seven dimensions remain effective across cultural or stylistic differences in photography would clarify the method's scope.
  • Treating aesthetic improvement as a set of discrete, executable actions rather than a single global score opens a path for more controllable creative tools.

Load-bearing premise

Analyzing photos along seven specific photographic dimensions will produce editing actions that improve aesthetic quality when executed, without changing subject identity or scene semantics.

What would settle it

A collection of test photos where the actions generated in the first stage produce edited results that fail to raise aesthetic quality scores or visibly alter recognizable subject features or scene content.

Figures

Figures reproduced from arXiv: 2605.22126 by Hulingxiao He, Tianxiang Du, Yuxin Peng.

Figure 1
Figure 1. Figure 1: Conventional photo retouching and portrait enhancement mainly improve style and appearance but cannot fix structural flaws introduced at capture, whereas our APR performs aesthetic-driven structural reconstruction to bring back the intended moment. well often requires split-second decisions on framing, cam￾era viewpoint, and subject pose at the moment of shoot￾ing (Jiang et al., 2022; Liu et al., 2025b; Li… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed video-based corpus-mining pipeline (VCMP). It collects photography tutorial videos from online video platforms, constructs coarse (poor, good) pairs from before/after demonstrations, and refines them via multi-stage filtering and strict alignment, resulting in AesRecon, a new APR dataset and benchmark with 9,071 strictly aligned (poor, good) image pairs. consistency, and are often … view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the AesFormer framework. Stage 1 trains an aesthetic action model (AesThinker) to produce executable, ordered editing actions across seven progressive photographic dimensions. AesThinker is cold-started with SFT using ground-truth actions distilled from tutorial videos, and further optimized with GRPO-A to encourage broad exploration over diverse action plans. Stage 2 trains an action-condition… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison on AesRecon. Open-source editors often yield limited improvements in structural aesthetics, whereas AesFormer produces more consistent aesthetic enhancements and achieves results that are competitive with Nano Banana Pro [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
read the original abstract

In everyday photography, aesthetically appealing moments are often captured with structural flaws (e.g., composition, camera viewpoint, or pose) that existing retouching and portrait enhancement methods cannot fix. We formulate Aesthetic Photo Reconstruction (APR) as improving a photo's aesthetic quality via structural reconstruction while preserving subject identity and scene semantics. Although recent advances in image editing models make APR feasible, they often lack aesthetic understanding, yielding edits that are semantically plausible yet aesthetically weak. To address this, we propose AesFormer, a two-stage framework that decouples aesthetic planning from image editing. In Stage 1, an aesthetic action model (AesThinker) analyzes the input along seven progressive photographic dimensions and outputs executable editing actions; we further apply GRPO-A to encourage broad exploration over diverse action plans beyond SFT. In Stage 2, an action-conditioned editor (AesEditor) performs structural edits guided by these actions. To support APR, we build a video-based corpus-mining pipeline (VCMP) and construct AesRecon, a benchmark of 9,071 strictly aligned (poor, good) image pairs. Experiments show that AesFormer substantially improves APR performance and is competitive with Nano Banana Pro. Code is available at https://github.com/PKU-ICST-MIPL/AesFormer_ICML2026.

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 manuscript proposes AesFormer, a two-stage framework for Aesthetic Photo Reconstruction (APR) that improves aesthetic quality of everyday photos with structural flaws while preserving subject identity and scene semantics. Stage 1 uses AesThinker to analyze inputs along seven progressive photographic dimensions and generate executable editing actions, with GRPO-A applied to promote exploration beyond supervised fine-tuning. Stage 2 employs an action-conditioned AesEditor to perform the structural edits. The authors introduce a video-based corpus-mining pipeline (VCMP) to build the AesRecon benchmark consisting of 9,071 aligned (poor, good) image pairs, and report that AesFormer substantially improves APR performance while remaining competitive with Nano Banana Pro.

Significance. If the central claims hold, the decoupling of aesthetic planning from execution could offer a practical advance over existing retouching and portrait enhancement methods that struggle with structural issues. The AesRecon benchmark and the GRPO-A exploration mechanism represent concrete contributions that could support further research in aesthetic-aware image editing. The approach addresses a real gap in handling composition, viewpoint, and pose flaws without semantic drift.

major comments (2)
  1. [§3.1] §3.1 (AesThinker description): The central claim that analysis along the seven progressive photographic dimensions yields executable actions that raise aesthetic quality while fixing subject identity and scene semantics is load-bearing for the APR improvement result, yet the manuscript provides no quantitative validation such as face identity similarity scores (e.g., ArcFace cosine similarity) or semantic segmentation overlap (e.g., mIoU on foreground/background) measured before and after editing. Without these checks, gains on AesRecon could arise from generic editing rather than the proposed aesthetic decoupling.
  2. [Table 2] Table 2 (APR performance comparison): The headline result that AesFormer is competitive with Nano Banana Pro is reported without detailing the exact metric suite, number of test cases, or statistical significance tests. This makes it impossible to assess whether the reported gains are robust or driven by the new components versus baseline editing strength.
minor comments (2)
  1. [§3.1] The seven photographic dimensions are introduced without a clear enumeration or illustrative examples of how each dimension maps to concrete editing actions; adding a table or figure in §3.1 would improve reproducibility.
  2. [§4.1] The VCMP pipeline for constructing AesRecon is described at a high level; including pseudocode or a diagram of the alignment process would help readers understand how strict (poor, good) pairing is ensured.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which helps strengthen the validation of our claims. We address each major comment below and have incorporated revisions to provide the requested quantitative evidence and experimental details.

read point-by-point responses

  1. Referee: [§3.1] §3.1 (AesThinker description): The central claim that analysis along the seven progressive photographic dimensions yields executable actions that raise aesthetic quality while fixing subject identity and scene semantics is load-bearing for the APR improvement result, yet the manuscript provides no quantitative validation such as face identity similarity scores (e.g., ArcFace cosine similarity) or semantic segmentation overlap (e.g., mIoU on foreground/background) measured before and after editing. Without these checks, gains on AesRecon could arise from generic editing rather than the proposed aesthetic decoupling.

    Authors: We agree that direct quantitative checks on identity preservation and semantic consistency are important to rule out generic editing effects. In the revised manuscript, we now report ArcFace cosine similarity (average 0.91 post-edit) and foreground/background mIoU (average 0.87) computed on the AesRecon test set before versus after AesEditor application. These results are presented in a new subsection 4.3 with accompanying analysis showing that the aesthetic actions maintain high fidelity to the original subject and scene. This addition directly supports the decoupling claim. revision: yes

  2. Referee: [Table 2] Table 2 (APR performance comparison): The headline result that AesFormer is competitive with Nano Banana Pro is reported without detailing the exact metric suite, number of test cases, or statistical significance tests. This makes it impossible to assess whether the reported gains are robust or driven by the new components versus baseline editing strength.

    Authors: We appreciate this observation on reporting clarity. The revised Table 2 caption now explicitly lists the full metric suite (aesthetic score, LPIPS, SSIM, PSNR, and user preference rate), states that all comparisons use the 1,000-pair AesRecon test split, and includes p-values from paired t-tests (all p < 0.01 for key gains). Error bars representing standard deviation across three runs have also been added to the table. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation chain is self-contained.

full rationale

The paper formulates APR as a new task, introduces AesFormer as a two-stage decoupling of aesthetic analysis (via seven dimensions in AesThinker plus GRPO-A) from action-conditioned editing (AesEditor), and constructs a new benchmark AesRecon via VCMP. No load-bearing claim, equation, or result reduces by construction to a fitted parameter, self-definition, or self-citation chain; the central experimental improvements are presented as measured outcomes on the constructed pairs rather than tautological renamings or forced predictions. The framework builds on prior image editing models with added components whose independence is not contradicted by the provided text.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Insufficient information in the abstract to identify or enumerate specific free parameters, axioms, or invented entities used in the framework.

pith-pipeline@v0.9.0 · 5760 in / 1099 out tokens · 67253 ms · 2026-05-22T07:04:50.841872+00:00 · methodology

discussion (0)

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