Deliverables
┌── (A) Task 1
│ ├── Screen Mockups
│ └── Design Rationale
│
├── (B) Task 2
│ └── Editing Ecosystem Analysis
│
└── (C) Task 3
└── Technical Report (containing documentation and model/dataset details)
Problem Statement
Inter IIT Tech Meet 14.0 - Adobe PS (Mid Prep)
Design and prototype a lightweight, mobile-first AI image editor for 2030 that demonstrates how creative editing can become faster, more intuitive, and energy-efficient on low-compute devices.
Your solution should combine design thinking, market research, and AI engineering across three integrated tracks:
Task 1: Product Design
Create a medium-fidelity mobile wire-frame (phone layout) showing your envisioned editor’s interface, navigation, and AI assistance flow.
Emphasize speed, clarity, and human-in-the-loop control, how the user sees what the AI did and how they can refine it.
Consider next-generation inputs such as gestures, stylus, voice prompts, or context-aware auto-suggestions.
Task 2: Understanding the Editing Ecosystem
Analyze the core tool set of modern image editors (crop, retouch, background removal, relighting, stylization, color balance etc.).
Identify which operations are already automated by AI and which remain
manual.
Select two key AI-powered features or editing workflows to implement in the
execution phase.
Example feature pairs:
- Object removal + background reconstruction (segmentation + in-painting)
- Lighting adjustment + style transfer (Adapter-based stylization)
- Automatic subject enhancement + color correction (retouching pipeline)
Task 3: Execution
Build the above selected, two AI editing workflows, each corresponding to one of your selected features.
Use open-source diffusion backbones (SDXL Inpainting, Flux 1.1, Kandinsky) or lightweight adapters (LoRA, quantized weights).
You can employ region selection modules (e.g., SAM, Mask R-CNN, matting models) to define editable areas.
Ideally, target lightweight inference and demonstrate via Stream-lit / web prototype.
1# Deliverables
2
3```
4┌── (A) Task 1
5│ ├── Screen Mockups
6│ └── Design Rationale
7│
8├── (B) Task 2
9│ └── Editing Ecosystem Analysis
10│
11└── (C) Task 3
12 └── Technical Report (containing documentation and model/dataset details)
13```
14
15- [Presentation](./Presentation.pdf)
16- [Demo Video](https://drive.google.com/file/d/1bvroGCCosPUEP54q06nFWq1LhNB4PiAf/view?usp=sharing)
17- [Optional Creative Artefacts](https://drive.google.com/file/d/1kSpf-y0LhQCn9-fF2gMLyodV8R3NkQeR/view?usp=sharing)
18
19# Problem Statement
20
21> **Inter IIT Tech Meet 14.0 - Adobe PS (Mid Prep)**
22
23Design and prototype a lightweight, mobile-first AI image editor for 2030 that demonstrates how creative editing can become faster, more intuitive, and energy-efficient on low-compute devices.
24Your solution should combine design thinking, market research, and AI engineering across three integrated tracks:
25
26### Task 1: Product Design
27
28Create a medium-fidelity mobile wire-frame (phone layout) showing your envisioned editor’s interface, navigation, and AI assistance flow.
29Emphasize speed, clarity, and human-in-the-loop control, how the user sees what the AI did and how they can refine it.
30Consider next-generation inputs such as gestures, stylus, voice prompts, or context-aware auto-suggestions.
31
32### Task 2: Understanding the Editing Ecosystem
33
34Analyze the core tool set of modern image editors (crop, retouch, background removal, relighting, stylization, color balance etc.).
35Identify which operations are already automated by AI and which remain
36manual.
37Select two key AI-powered features or editing workflows to implement in the
38execution phase.
39Example feature pairs:
40
41- Object removal + background reconstruction (segmentation + in-painting)
42- Lighting adjustment + style transfer (Adapter-based stylization)
43- Automatic subject enhancement + color correction (retouching pipeline)
44
45### Task 3: Execution
46
47Build the above selected, two AI editing workflows, each corresponding to one of your selected features.
48Use open-source diffusion backbones (SDXL Inpainting, Flux 1.1, Kandinsky) or lightweight adapters (LoRA, quantized weights).
49You can employ region selection modules (e.g., SAM, Mask R-CNN, matting models) to define editable areas.
50Ideally, target lightweight inference and demonstrate via Stream-lit / web prototype.