Multimodal & Spatial Computing · core
Image & Video Generation
Diffusion and transformer generation, conditioning, latent spaces, control, consistency, safety, and media evaluation.
Mental model
Generative media iteratively maps noise or tokens into structured outputs under conditioning. Quality requires prompt/control alignment, temporal consistency, safety, and perceptual evaluation.
How to study Image & Video Generation
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use Denoising Diffusion Probabilistic Models to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Image & Video Generation with Pose & Motion Tracking, Local & On-device Inference. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Design exercise: Image & Video Generation and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
Practice and explain it back
Design exercise: Image & Video Generation
Diffusion and transformer generation, conditioning, latent spaces, control, consistency, safety, and media evaluation. Implement designOutline() returning non-empty values for: conditioning, generationProcess, qualityEval. Each value must name a concrete mechanism or decision.
Expected evidence: A design outline with conditioning, generationProcess, qualityEval plus an explicit failure mode or trade-off.
Open the interactive drill →Review prompts
- Why is temporal consistency harder in video generation than image quality?
Build evidence
Synthesize: Multimodal & Spatial Computing
Connect vision, audio, generation, on-device intelligence, robotics, spatial interfaces, and HCI. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks