Shawnrussell

Professional Introduction for Shawn Russell
Research Focus: Cross-Style Dance Motion Transfer Systems

As a researcher specializing in AI-driven dance style migration, I develop computational frameworks to decompose, analyze, and recombine movement aesthetics across dance genres—preserving artistic intent while enabling novel choreographic synthesis.

Core Innovations

1. Style Disentanglement & Transfer

   • Designed hierarchical motion encoders to isolate style (e.g., flamenco’s footwork dynamics) from content (basic step sequences), enabling cross-genre transfers (ballet→hip-hop).

   • Introduced temporal attention gates to handle asynchronous style-content pairs (e.g., adapting Bharatanatyam’s rhythmic patterns to contemporary dance).

2. Ethical Motion Representation

   • Curated culturally annotated datasets with indigenous practitioners to avoid appropriation (e.g., distinguishing sacred Māori haka from derivative forms).

   • Proposed provenance tracking for synthetic choreography, embedding lineage metadata in motion-capture data.

3. Real-World Applications

   • Augmented Performance: Tools for dancers to "try on" styles digitally before physical rehearsal.

   • Cultural Preservation: Stylistic archiving for endangered dance forms via generative repositories.

Methodologies:

• Hybrid architectures (Graph CNNs + Transformers) for 3D pose style manipulation.

• Perceptual loss functions trained with professional dancer feedback.

Vision: To expand human creativity through AI, not replace it—where technology dances in service of art.

Optional Customization:

• Add specific tools (e.g., Blender-Mixamo pipelines).

• Include collaborations (e.g., Laban Movement Analysis experts).

• Short pitch: "I make AI understand the difference between a pirouette and a pop-and-lock. Let’s redefine dance tech!"