The paper “Morphology independent representation of motions for interactive human-like animation” describes a method of skeletal representation that is dimensionless, and so “morphology independent”. Additionally, the authors developed a technique for approximately solving constantly-updating constraints in real time. “Spatial relationship preserving character motion adaptation” describes an interaction mesh that is designed to maintain relationships between the locations of characters’ joints who are interacting closely with (but maybe not quite touching) one another. By minimizing the Laplacian deformation of this mesh subject to bone length, position, and collision constraints, the authors were able to maintain the “high-level” semantics of motion sequences after changing the sizes of characters.

Both techniques are “morphology independent”, and well suited to a rapidly changing environment or interactive application with multiple characters. Both have their limitations. The mesh technique seems to be more robust overall (the other technique fails when a character is upside down), but it is limited, to some extent, to close quarters or close interactions. If the environment is too sparse then there presumably won’t be enough vertices to create an interaction mesh. Both techniques seem to struggle with properly positioning the center of mass of the characters. Honestly, the authors of the “morphology independent” paper don’t do a very good job selling their technique as something that could have any commercial application. They mention more than once the danger of a “inhomogeneous repartition of the deformation along the kinematic chain”. (I don’t know what that means but it doesn’t sound good.) The other technique seemed to work very well for a specific set of motions. I liked that the authors used spacetime optimization so that characters could anticipate motions a few frames in the future. This technique could probably be applied to gaming, or maybe there could be some ergonomics application that involved studying different peoples’ movement in restrictive environments.

MKM (1) proposes a framework that is based on a morphology-free representation of a character, wherein the skeleton is represented using half-planes, a spline and normalized segments, that allows operations such as motion sync, blending, retargeting and adaptation. This method is useful when you don’t have a large motion database or when the motions can be varied (user driven) and allows motion synthesis at interactive rates (30Hz).As the authors mentioned, crowd simulation is a natural application apart from VR & e-learning. It doesn’t do a good job with penetration and can lead to unrealistic motions as a result of neglecting the mass.

3 proposes the idea of an interactive mesh that enables more realistic editing and retargeting of motion that has a number of close interactions. The interaction mesh is essentially a data structure that captures the spatial relationships between objects/body parts and tries to minimize the deformation of this mesh locally (taking into account the previous frames) by using a spacetime optimization subject to positional, bone-length and collision constraints. As a result, the animator doesn’t need to modify the constraints while editing/retargeting, since they are “captured” via the interaction mesh. The computation is real time when there are very few “interacting” characters and becomes a natural favorite for games like wrestling and fighting!

The Multon/ Kulpa, paper introduces a approach that is suited for real time animation of interactive environments due to it’s reduced computational cost. The key ideas are to normalize the skeleton to contain only one body segment and separate upper and lower limbs (upper arm/thigh)and represent the spine as a spine and consider the segments as a kinematic chain. Another key idea is storing the constraints together with the motion. An iterative process is used to model the motion, but its claimed to take only few computation times to solve for the simplified skeleton. The fast computation time is a definite advantage of the new representation, but I think due to simplifying the structure too much they lose some naturalness in the motion. So this method would better suited for modeling crowds/ background animation than the main character.

The Edmond / Komura Paper introduces a way of using a interaction mesh for a motion by assuming that the mesh characters are rigged with skeletons, and each body segment is bounded by a volume. Postures of the characters are represented by the positions of the joints, rather than the joint angles. A spacetime optimization problem is solved to adapt the motion at each morph-step and at every morph-step, the body sizes and the positional constraints are updated, and the motions of the characters are adapted by minimizing the sum of the deformation acceleration and constraint energy. The method used in the paper performs better than using kinematic constraints and collision avoidance for motions that involves many close interactions. The method seems fail when the constraints are drastically different from those in the original motion, such as when one of the interacting characters is scaled too small or too large. Using the interaction mesh could be used for modeling many kids of close interactions such as hand to hand combat dancing and navigating through obstacles and working with tools or utensils.

1 – The core idea in the paper is to use a representation that is independent of the morphology of the character and are more adaptable than joint angles for motion re-targeting. The skeleton is simply represented using a bunch of normalized segments, half-planes and a spline and can be scaled easily for another character – thus eliminating the need for a large database. The priority associated with ‘heaviness’ of the groups is pretty interesting and according to the paper produces realistic results. However this method fails to take dynamics into account. This simplistic method is therefore suitable for secondary characters which do not draw much attention from the viewer.

3 – This paper introduces the idea of interactive mesh, the use of which allows more realistic interaction of a character with it’s environment along with automatic constraint preservation. This allows motion to be retargeted to scaled versions of same characters without the animator having to manually alter the constraints. This produces impressive results under difficult constraint cases while maintaining balance and natural movement. However computing the mesh is computationally very expensive and one can expect them to be even more complicated for a larger more detailed character. Therefore this is applicable to scenes in which there are less background characters. While watching the videos what caught my eye more than once was that it produced unrealistic outputs. In the Judo example, inspite of the opponent being scaled up and the other scaled down, this lean thin guy is able to tackle the heavy guy very easily. Isn’t the mass associated with the mesh also scaled up?

Paper two presents a characterization for capturing a more generalize essence of the overall motion. Motions are represented as normalized hierarchical collections of bones and half-planes to facilitate motion adaptation. The method does make real-time adaptation of the motion of a character simple and the hierarchical frameworks defined therein help to induce natural motion. However, the paper acknowledges limitations of the lack of control over center of mass results in difficulties in creating realistic motions, explicit attention is not really given to collision detection, and it appears a lot of user effort must go into defining constraints. At first glance, these methods seem like they would be very useful for adapting a particular piece of motion data to a scenario with a well-defined set of constraints.

Paper three provides a different spin on retargeting. The paper focuses on the preservation of relative spacing between key figures in an animation. A volume is defined between joint positions of two actors and the deformation of this volume is minimized as the different motions are retargeted. This method supports simple computation of the relative motion of characters by using the joint positions instead of angles. However, as mentioned in the work, extreme changes in scale over a tightly bound volume would break this method. Also, overly complicated volumes could bog down the computation in real-time environments. This method would be incredibly useful in cases where the real-time relative motion between characters is important. It may provide an efficient real-time method for computing motion in an environment with relatively few constraints where character interaction is key to the scene; especially in video games and in retargeting to non-traditional character shapes.

Representation:
Body is divided into kinematic sub-chains using: variable length limbs, normalized segments, spline spines, and half-planes; some limbs are not encoded.

Pros:
-capable of retargeting motion to any arbitrary skeleton without using classical IK
-solves kinematics for modifying gestures to adapt to continually changing constraints in the virtual world
-doesn’t require a large database of motion data
-responsive to user specified start/stop of motion

Cons:
-physical dynamics are not taken into account
-cannot control jumps realistically
-the constraint solvers seem incapable of compensating for postures that don’t maintain balance; this may be limiting (probably in a good way, but it is still limiting)
-highly dynamic motions are not well represented

Use:
I could see this representation being used for a lower-end simulation of environments with mostly passive characters. The main issue with this representation appears to be its ability to handle highly dynamic motions. So using this representation for avatars (with unique morphology) in a social VE such as “Second Life” would be ideal; a new level of environmental responsiveness could be added to the avatars while computation could be kept low enough to enable interaction of many characters.

Spatial Relationship Preserving Character Motion Adaptation:

Representation:
Uses a volumetric interaction mesh defined by the joints/vertices of characters/objects with which the characters are interacting; this is computed for every time frame. This mesh is retargeted to different characters/objects by maintaining local details of the computed mesh.

Pros:
-preserves spatial relationships between characters or environments for close -interactions
-can retarget semantics of motion to characters with different morphologies

Cons:
-cannot accurately represent narrow body parts
-small morph steps are needed to deter artifacts
-penetrations can occur with environments that are too constrained/small
-unstable when original motion contains movements where body parts pass through each other
-O(m * n) complexity; m = vertices in mesh, n = number of frames

Use:

First of all, I find the underlying idea of this method to be extremely promising in terms of video games. Even today, close interactions between characters in video games is severely lacking; penetration of geometry in these situations seems to be accepted as a norm (sometimes ‘hidden’ with particle effects or improbable collisions). Unfortunately, this technique doesn’t seem suitable for real-time yet due to the complexity and lack of scalability (between multiple objects/characters). At this point, I think this method will best be used as a tool for offline animation of close interaction for a single motion to be mapped to different characters. For instance, if a developer wanted to implement an improved, yet general, melee system (same attacks/motions) for a game with multiple character types or races (I’m thinking World of Warcraft or The Elder Scrolls), this technique would greatly expedite the process of defining these ‘static’ motions for the multiple types.

The “Morphology-independent” paper provided a way to take motion data and combine it into a new form of the motion which includes constraints built in to the representation. The feet can be forced to be on the ground or wrists above a certain level for instance. For motions between two models, it also allows adding a positional constraint for one model that is itself tied to the position of a different model.

The “Spacial Relationship” paper is directed more towards close interactions between models and other objects. It attempts to reduce the intersection of two models with the goal to keep them close to touching but not going through each other. It can also handle two different parts of the same model interacting.

In general, the Morphology paper seems like it would be more useful for helping with basic constraints such as positioning of an arm or leg. The Spacial Relationship paper seems more useful for complex situations where the approximate motions are already defined and fine tuning of the exact position is needed for interactions. So when subtle changes in interactions are needed, the Spacial Relationship ideas might be more useful. When larger changes in a specific portion of the model are needed, the ideas from the Morphology paper might be better.

Unfamiliar terms : Cyclic Coordinate Descent Algorithm.

Basic ideas: Adapting motions to new space-time constraints in real-time is perhaps not possible with large database of motion data. The authors aim at controlling the motion with small motion capture database.

1. Apply IK to a subset of kinematic chain. Use intermediate skeleton (and Normalized ) that has less degree of freedom. Human body is subdivided into kinematic sub-chains ( Six groups ).

2. Prioritize the constraints. The constraints with low priorities are only verified after those associated with higher priorities.

3. Use specific constraint solver which take advantages of heuristics in iterative search process.

Comments: At first reading, the whole ideas seems promising but the authors have clearly mentioned the limitation of their approach. I am not quite sure how much computational saving someone can expect on modern hardware by looking at figure 10 where only elbows and Knees are simplified.

But this has advantage where real-time interaction is the main goal because of less
computational requirements.

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Paper : Spatial Relationship Preserving Character Motion Adaptation.

Unfamiliar terms:
(1) Laplacian deformation techniques
(2) Laplacian Coordinates.
(3) Gauss linking Integral.
(4) Topological Coordinate ( Very strange term ).

Comments:

Volumetric meshes have been used for varieties of applications. But I am not certain about the word “a new representation” in this paper. Are they storing only the “Interaction Mesh” in their file format or the mesh is generated only for collision detection using classical joints information. So is it primary or secondary representation ?

I am also not quite sure how rigid is the spatial relationships between two actors. Are they allowed to act independently ? If it is soft, then how it is automated ?

But, I can see some big advantages of this representation over others

(1) it could be very powerful tool to synchronize motions among multiple actors
(for example in choreography ).

(2) Anticipation: One actor can well prepare in advance for the future actions based on proximity information gained from the mesh.

But the great flexibility provided by this representation could also be bane, as it could be easily abused to produce unrealistic motion.

#2 essentially presents a simplified, what they call normalized, skeleton as an alternative to the standard skeletal representations of human motion. Constraints are stored with the motion, and can also be specified interactively. First the skeleton is converted to the normalized skeleton; adapted to the specific character’s size, environment, and constraints; and then converted back into a standard skeleton. This system seems most applicable to scenes with lots of characters interacting with each other, since the computations have all been simplified. One thing that wasn’t clear to me is how they make sure motions preserve a certain style or naturalness. The authors seem more concerned with simply getting the joints to their constraints in the quickest and most computationally efficient manner possible.

In the third paper the authors introduce the concept of an interactive mesh for preserving spatial relationships in a scene. For example, in a wrestling motion, they show how they can make one guy fat and the other one skinny and the motion still looks natural and correct. This seems to be a great idea to me, but I think they might be overselling a bit how often we want to be constantly preserving spatial relationships in our scenes above all other things. For example, they show how moving one character’s hand forward will have the side effect of moving the other character’s head proportionally backward. This system obviously wouldn’t work well if we wanted to change the motion to have the first character’s hand strike the second character’s head. Another drawback that I found interesting in light of our class conversations was the authors’ comment on how they tend to oversmooth the motions with their acceleration energy term.

Not sure if this is linked elsewhere, but here are some videos from Richard Kulpa’s website showing off some concepts presented in the MKM papers: http://www.irisa.fr/bunraku/Richard.Kulpa/

3: The second method is targeted at close proximity interactions between a character and the environment (including the character itself). The advantage of this method is that it can handle such close interactions with characters that have been rescaled from the original motion capture. This suggests the ability to have alien or non-human characters interact reasonably realistically.

Interestingly both methods claim to handle scaled characters better than traditional methods, and to do so at real-time/interactive speeds. Where the first paper aims to extend motions from a small database, the second method tries to preserve existing close interaction. The first method could be desirable in situations where budget to produce motion capture data is limited, or only a set amount of data is available. The second method seems less interested in extending motion and more in adjusting them to fit new characters.