Hot off the Wires 🔥

We're eavesdropping on the smartest minds in research. 🤫 Don't miss out on what they're cooking up! In this section, we dissect some of the juiciest tech research that holds the key to what's next in tech.⚡

Remember playing fighting games like Mortal Kombat or Street Fighter? Those epic battles where characters throw punches, dodge kicks, and perform perfectly choreographed moves?

If you've been following our newsletter, you might recall our recent coverage of NVIDIA's MaskedMimic - a breakthrough in generating single-character motion. But here's the thing: making two characters interact naturally? That's a whole different ball game!

Creating realistic interactions between two characters in virtual worlds is an art that's as complex as choreographing a real dance duet!

Think “Person A kicks towards Person B” or “Person A sneaks up behind Person B.” The complexity isn’t just in individual motion—it’s in the interplay, the reaction, and the subtle adjustments one makes based on the other.

Enter InterMask, a groundbreaking framework from the University of Alberta and Snapchat researchers that's taking character interaction generation to the next level!

Forging the fundamentals

Before we dive in, let's decode some key terms:

So, what’s new?

While models like diffusion-based Com-MDM and InterGen have elevated single-character motion, two-person interactions present unique hurdles:

• Realism is elusive due to overlooked spatial dependencies.

• High-quality results require computationally expensive sampling processes.

• Existing methods struggle to fully capture the nuanced back-and-forth dynamics of interaction.

Previous methods often struggled with spatial awareness – leading to awkward moments like characters passing through each other (ouch!).

This novel framework of InterMask uses a two-stage approach to model interactions with superior spatial and temporal coherence. It ensures that interactions remain natural and physically plausible.

Under the hood…

InterMask works its magic in two stages:

VQ-VAE for Motion Representation

First, it uses something called a VQ-VAE (Vector Quantized Variational Autoencoder) to transform complex motion sequences into a simpler format.

Here’s how this works:

• Motion data, represented as pose sequences (joints with positions, velocities, and rotations), is fed into the VQ-VAE.

• This data is compressed into a latent space, then mapped onto a learnable “codebook,” creating a Motion Token Map—a structured representation of movements over time.

• 2D convolutions are used to maintain the spatial-temporal richness of interactions.

The compression process balances spatial fidelity (how individuals occupy 3D space) with temporal flow (how they move through time), paving the way for realistic interactions.

During training, the model uses two main objectives:

1. Vector Quantization Loss: This helps the model accurately recreate motion and ensures the motion data is stored efficiently in a set of fixed codes.

2. Geometric Loss: This ensures the predicted motion follows natural joint movements and maintains realistic distances between connected joints.

Inter-M Transformer for Interaction Modeling

Then comes the clever part – the Inter-M Transformer, which processes the tokens produced by the VQ-VAE to produce output motion sequences.

It's like having an AI choreographer that understands how two people should move together.

Here, the researchers introduced innovations at two levels:

• Innovative masking techniques:

  • Random Masking: Randomly hides parts of the motion tokens to train the model to fill gaps intelligently

  • Interaction Masking: Focuses entirely on one individual’s motion while masking the other, forcing the model to understand mutual influence

  • Step Unroll Masking: Iteratively re-masks uncertain predictions for refinement, for improving predictions

• Innovations in the Transformer blocks:

  • Shared Spatio-Temporal Attention: Ensures the model grasps how individuals share physical space over time.

  • Cross-Attention Layers: Captures how one person’s actions affect the other.

  • Text Conditioning: Interprets input prompts (like “Person A punches Person B”) using CLIP-based embedding for contextual realism.

Inferencing

The inference process begins with a fully masked sequence and a text prompt describing the interaction.

First, the Inter-M transformer generates all tokens for each individual over multiple iterations. Next, the tokens are dequantized (using the ‘codebook’ in reverse) and decoded to generate motion sequences using the VQ-VAE decoder.

This efficient approach keeps computational demands low:

• Parameters: Only 74M, significantly lighter than most models in its league.

• Inference time: Just 0.8 seconds, making it both fast and scalable.

Why does this matter?

Here’s why InterMask is making waves:

• State-of-the-art realism: It outperforms existing models on various datasets with fluid, natural motion

• Human evaluation: Comprehensive user studies on Amazon Mechanical Turk reveal that users prefer InterMask’s outputs to prior methods

• Robustness: Works well across body types and scales effectively with data

Check out some of these cool results 👇🏼

The key highlight of this approach is InterMask's capability to perform the reaction generation task, where the motion of one individual is generated depending on the provided reference motion of the other, with and without text descriptions.

InterMask is already showing promise across multiple industries:

• Game developers like Epic Games and Ubisoft can use it to create more realistic character animations and interactions in video games

• Animation studios like Pixar could speed up their character interaction animation workflows

• VR companies like Meta could enhance social VR experiences with more natural avatars

• Even robotics companies could use it to teach robots more natural human interaction patterns

Next time you watch two characters interact seamlessly in a game or movie, remember - there might be a bit of InterMask magic making it all possible! 🎮✨

What do you think? Let Spark and Trouble know—they love hearing about what excites you in tech!

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Spark 'n' Trouble Shenanigans 😜

Ever wondered what AI thinks you look like based on your social media persona? Well, Trouble couldn't resist putting this to the test!

After seeing a hilarious Reddit thread where ChatGPT roasted someone's appearance based on their posts (talk about AI throwing shade! 😆), our data-loving troublemaker decided to feed some of his LinkedIn posts to ChatGPT.

The result? A surprisingly on-point visualization that captured Trouble's vibe (though Spark insists the real Trouble is way cooler 😎).

We're pretty sure adding that "reasoning" prompt helped avoid any AI sass – though we kind of missed out on the roast!

Ready to see if AI can nail your digital doppelganger or hilariously miss the mark?

Give it a shot and share your results with us! Refer to this sample below 👇🏼