Learning to Infer Graphics Programs from Hand-Drawn Images (1707.09627v5)

Abstract: We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of \LaTeX. The model combines techniques from deep learning and program synthesis. We learn a convolutional neural network that proposes plausible drawing primitives that explain an image. These drawing primitives are like a trace of the set of primitive commands issued by a graphics program. We learn a model that uses program synthesis techniques to recover a graphics program from that trace. These programs have constructs like variable bindings, iterative loops, or simple kinds of conditionals. With a graphics program in hand, we can correct errors made by the deep network, measure similarity between drawings by use of similar high-level geometric structures, and extrapolate drawings. Taken together these results are a step towards agents that induce useful, human-readable programs from perceptual input.

• The paper introduces a novel two-step method that converts hand-drawn images into high-level graphics programs using CNNs for spec extraction and SMC for synthesis.
• The approach leverages amortized inference to optimize search policies, achieving a Top-1 accuracy of 67% on benchmark hand-drawn figures.
• The method enhances error correction and pattern extrapolation, paving the way for real-time sketch-to-graphics conversion in design and education.

Summary of "Learning to Infer Graphics Programs from Hand-Drawn Images"

This paper, authored by Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, and Joshua B. Tenenbaum, presents a novel approach to generating graphics programs from 2D hand-drawn images. The model aims not just to reproduce drawings but to abstract them into high-level symbolic programs written in a subset of LaTeX. By leveraging convolutional neural networks (CNNs) and program synthesis techniques, the authors propose a two-step approach involving the inference of drawing primitives and subsequent program synthesis.

Key Contributions

1. Model Architecture:
   • The model uses a CNN to parse a hand-drawn image into drawing primitives (a specification or "spec"). These primitives include basic shapes such as rectangles and circles.
   • The specs are then input to program synthesis techniques, which generate high-level graphical programs. These programs employ constructs like iteration, symmetry, and reflection, enabling the encapsulation of complex visual phenomena.
2. Inference Technique:
   • The approach combines deep learning with stochastic search methods, specifically Sequential Monte Carlo (SMC), to infer specifications from images.
   • The strategy emphasizes amortized inference, utilizing trained neural networks to suggest likely specs rapidly.
3. Amortized Program Synthesis:
   • To address the computational expense of program synthesis, the authors introduce a learning framework to optimize search policies, thereby reducing inference times.
4. Applications:
   • Error Correction and Robustness: By synthesizing programs, the model can refine and adjust inferred specs, leveraging the structure of graphical programs to correct initial errors and increase accuracy.
   • Similarity and Extrapolation: The methodology supports novel applications such as measuring similarity between drawings based on high-level features and extrapolating patterns in a principled manner.

Experimental Validation

The authors demonstrate strong empirical performance of their approach. Their model achieves robust generalization from synthetic art to real, noisily hand-drawn images by training with noise-injected synthetic data. On a benchmark of hand-drawn figures, the system achieves a Top-1 accuracy of 67%, interpreted as a match between the top program hypothesized by the model and the ground truth once program structure is considered.

Implications and Future Directions

The implications of this research lie at the intersection of AI and computer vision, extending traditional raster-based image processing with symbolic reasoning through program synthesis. The approach can dramatically enhance the precision and interpretability of machine representations of visual data. Furthermore, it opens pathways for the development of software tools capable of transforming loose sketches into polished graphical representations—valuable in fields such as design and education.

Future research may focus on expanding the richness of DSLs (Domain Specific Languages) to capture more sophisticated visual concepts, reducing synthesis times to enable real-time applications, and exploring cross-domain adaptations of neural network-guided program induction techniques.

In conclusion, this work represents a significant step towards bridging perceptual and cognitive models, merging vision with algorithmic program synthesis, and reinforcing the potential of AI to interpret and generate structured, programmatic explanations of the visual world.