Hey everyone,
We’re developing an AI-driven product photography solution that goes far beyond simple background replacement. Our goal is to seamlessly integrate real product images into AI-generated environments, ensuring precise lighting, perspective, and reflections that match the scene without breaking realism.
While current tools like Stable Diffusion, ControlNet, and GAN-based solutions provide great generative capabilities, we’re looking for deeper insights into the technical challenges and best approaches for:
- Product Integration:
- How do we ensure the product remains unchanged while blending naturally into AI-generated environments?
- What are the best ways to preserve surface textures, reflections, and realistic depth when compositing a product into a generated scene?
- Any thoughts on HDR-aware compositing or multi-view product input for better 3D grounding?
- Environmental Enhancement:
- What methods exist for AI-driven relighting, so the inserted product adopts scene-consistent lighting and shadows?
- Can we dynamically match materials and reflections so the product interacts with its AI-generated surroundings in a believable way?
- How would scene-aware depth estimation improve integration?
- Bridging Product & Environment:
- What role can SAM (Segment Anything Model) or NeRF-like techniques play in segmenting and blending elements?
- How can we use ControlNet or additional conditioning methods to maintain fine-grained control over placement, shadows, and light interaction?
- Would a hybrid approach (rendering + generative AI) work best, or are there alternatives?
We’re open to discussing architecture, model fine-tuning, or any practical insights that could help push AI-generated product photography closer to real-world studio quality.
Looking forward to hearing your thoughts! 
P.S. If you have experience working with Stable Diffusion, ComfyUI workflows, or other generative visual AI techniques, we’d love to connect!