Aesthetic image cropping aims to identify the optimal crop of an image in terms of aesthetics and composition. While supervision based on annotated data is fundamental, the field has been hindered by a long-standing problem: existing datasets suffer from (1) human subjectivity and (2) rigid discreteness confined to fixed sampling grids. These flawed annotations not only limit the accuracy and generalization of trained models but also severely distort fair evaluation. To overcome this, we propose to model human cropping preference as a multi-peaked, continuous, and sharp field over the crop space. We introduce the Continuous Preference Field (CPF), which recovers a dense preference landscape from discrete annotations through (1) peak clustering, (2) off-lattice refinement, (3) negative shaping, and (4) field assembly. Based on this, we train CPIC, a VLM-based cropping model optimized via GRPO with the CPF reward, which overcomes template collapse, achieving state-of-the-art performance and exceptional out-of-domain generalization. Finally, to resolve the long-standing benchmark evaluation crisis, we introduce CPICD, a comprehensive recalibration of existing ground-truth boxes. By leveraging the CPF to correct grid-bound artifacts across mainstream benchmarks, CPICD establishes a rigorous and reliable foundation for future cropping research. Extensive experiments and user studies demonstrate the superiority of our CPF, CPIC, and CPICD. Code, model, and data are available at https://github.com/zzqingz/CPIC.
Figures & tables
Figure 1: Illustration of three key properties of our Continuous Preference Field (CPF) : (1) multi-peakness, (2) continuity, and (3) sharpness. (Zoom-in for best view)
Table 2
Figure 2: Overview of CPF construction and its applications. (1) Peak clustering identifies distinct preference modes from human-scored crops. (2) Off-lattice refinement adjusts each peak using preference, composition, and subject-preservation scores. (3) Negative shaping constructs five types of undesirable crops. (4) Field assembly combines peak-distance and negative gating into CPF. CPF serves as the reward for GRPO training of CPIC and guides annotation recalibration for CPICD.
Figure 3: Comparison of original benchmark annotations and CPICD recalibrated boxes. Recalibration improves unbalanced compositions with truncated subjects or overly narrow crops.
Table 3: Comparison on the four benchmarks. Best per column in bold , second underlined .
Figure 4: Qualitative results on in-domain (GAIC) and out-of-domain (FCDB) images.
Configuration
CPF ingredient
GAIC
FLMS
FCDB
Boxes ↑
multi-peak
continuity
sharpness
SRCC ↑
IoU ↑
LAION ↑
IoU ↑
Supervision strategies
(a)
CE on annotated GT
0.523
0.798
5.060
0.642
56
(b)
+ GRPO w/ IoU
0.564
0.822
5.074
0.671
92
(c)
CE on refined peaks
✓
0.538
0.827
5.076
0.704
154
(d)
+ GRPO w/ IoU
✓
0.559
0.835
5.082
0.701
199
Table 6: Ablation studies. Boxes counts the distinct crops the model emits over the 200 GAIC test images (at most 200 ), the diversity measure of Table 2 .
Appendix figures & tables11 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 5: Zero-shot interpretable and interactive aesthetic cropping. Top: CPIC provides a concrete rationale grounded in the selected subject, spatial arrangement, contrast, and visual effect. Bottom: after its initial crop, CPIC follows free-form feedback to remove the distracting foreground pedestrian and tightly reframe the three musicians.
Output target
GAIC
FLMS
FCDB
ACC 10↑
IoU ↑
IoU ↑
Box only
0.905
0.842
0.714
Short text + box
0.910
0.822
0.723
Appendix
Table 7: Output-target ablation. Best bolded .
Coordinates
GAIC
FLMS
FCDB
12×12 anchors
1.0000
0.9099
0.8273
0 – 1000 integers
0.9994
0.9997
0.9989
Appendix
Table 8: Oracle IoU on three benchmarks.
ID
Config.
GAIC
Comp.
Subj.
GAIC
FLMS
FCDB
SRCC ↑
IoU ↑
IoU ↑
(a)
None
–
–
–
0.517
0.829
0.672
(b)
Direct
–
–
–
0.526
0.812
0.671
(c)
Refine
✓
–
–
0.563
0.827
0.704
(d)
Refine
✓
✓
–
0.578
0.828
0.709
(e)
Refine
✓
✓
✓
0.590
0.842
0.714
Appendix
Table 9: Refinement-score ablation.
Figure 6: CPF scores and IoU for pairs of crops. CPF distinguishes coherent compositions from visually flawed alternatives that IoU fails to adequately penalize, providing qualitative evidence of its closer alignment with human visual preference.
Figure 7: User study interface for cropping quality.
State Key Laboratory of Mobile Network and Mobile Multimedia Technology, ZTE Corporation · School of Data Science and Institute of Artificial Intelligence, Chang’an University
Faculty of Data Science, City University of Macau, Macao, China · Shenzhen University of Advanced Technology, Shenzhen, China · Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China +2